Instructions to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Ollama
How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0
```

Lemonade

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-35B-A3B-Distill-MTP APEX-I-MiniPlus-V2.1 GGUF

The Definitive Frontier MoE · Blistering +24 to 28+ tok/s with System RAM Offload · Full 256K Context on 24GB Workstations

🔓 LOOKING FOR THE UNCENSORED / ZERO-REFUSAL RELEASE?

If your workflow involves red-teaming, penetration testing, systems security research, or unrestricted generation, check out the official sister edition: 🔗 Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF — authentic Heretic TPE directional abliteration with zero moralizing refusals, identical 14.65 GB footprint, dedicated Q8_0 MTP draft head & mmproj vision companions, and empirically verified 5.1280 WikiText-2 perplexity.

THE DEFINITIVE SPECIFICATION IN THE 13–14 GB CEILING

This APEX-I-MiniPlus-V2.1 release represents the absolute technological limit of sparse Mixture-of-Experts quantization within the 13–14 GB envelope. Every single tensor of its 40 layers and 256 micro-experts has been mathematically audited to maximize reasoning precision, eliminate recurrence state drift, and prevent AVX2 CPU dequantization stalls.

🏆 THE QUANTIZATION SWEET SPOT: Q5–Q6 FIDELITY AT 3-BIT FOOTPRINT

Why APEX-I-MiniPlus V2.1 outperforms standard community quants:

Empirical WikiText-2 Perplexity: 5.3952 ± 0.1246 (evaluated directly on GGUF binary, 2048 ctx, 10 chunks) — near-zero loss (ΔPPL ≈ +0.05) from unquantized baseline (approx. 5.34).

Q5–Q6 Quality at 3-Bit Footprint: Measured at ΔPPL ≈ +0.05 for this GGUF; Delivers reasoning fidelity and syntax consistency typical of Q5_K / Q6_K while occupying only approx. 14.7 GB (same featherweight footprint as Q3_K_M).

Zero Routing Drift: 100% of expert routing matrices (gate_inp) remain in uncompressed F32, ensuring tokens are dispatched to the exact right experts on every forward pass.

DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!

Regardless of release version (whether V1, V2, or V2.1), NEVER confuse handcrafted APEX-I-MiniPlus builds with generic community APEX-I-Mini releases:

Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit IQ2_S (dropping below the critical quality floor), leaves the sensitive token output head unarmored at 3-bit Q3_K_M, and compresses attention projections down to Q3_K. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.

Handcrafted APEX-I-MiniPlus (All Editions by IsValorum): Every single MiniPlus release—from V1 and V2 to V2.1—is a custom tensor-by-tensor architecture that preserves uncompressed F32 router gates, armors the token output head in high-precision Q6_K, safeguards attention gates in Q8_0, and keeps core reasoning experts at or above calibrated 3-bit (IQ3_XXS/IQ3_S). Even our earlier builds vastly outperform generic community APEX recipes and flat 3-bit quants.

Optimization History & Transparency Notice

We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures:

Specification	Core Experts (10–29)	Edge Experts (0–9, 30–39)	Shared Expert (`shexp`)	Full Attention (L3, 7, 11, ...)	Attention Gates (30 Layers)	Output Head (`output.weight`)	Routers (`gate_inp`)	Size / Overhead	Real-World Impact
Generic APEX Mini	`IQ2_S` (2.50 bpw)	`Q3_K` (only 5 layers)	`Q4_K` / `Q3_K`	`Q3_K`	Compressed	`Q3_K_M`	Compressed	Baseline (approx. 12.5 GB)	Severe syntax errors, broken code indentation, high perplexity in `<think>`.
MiniPlus V2.1 (CURRENT)	`IQ3_XXS`	`Q3_K` (10 layers)	`Q5_K` (All 40 layers)	`Q4_K` (`q/k/v`) + `Q6_K` (`output`)	`Q8_0`	`Q6_K`	`F32`	Definitive Build (approx. 13.74 GiB)	Zero AVX2 CPU stalls; +24 to 28+ tok/s streaming when offloading bulk of the model to system RAM (DDR4/DDR5). Dedicated Q8_0 MTP draft head enables ultra-fast speculative execution.

Deployment & System Architecture Guide

Full GPU VRAM Offload (24GB+ VRAM, -ngl 99): Effortless full offload with native 256K context support. Blistering throughput on RTX 3090 / 4090 / 5090 GPUs.

System RAM Streaming Specialist (DDR4/DDR5 & Massive Context): Specially engineered to run either partially or entirely out of system RAM across large or full (+160k to 256k) context windows. By replacing non-linear codebooks with linear SIMD-optimized Q3_K edge experts and upgrading shared foundation experts to Q5_K across all 40 layers, AVX2 CPU dequantization stalls are completely eliminated (+24 to 28+ tok/s). Depending on your processor architecture and memory bandwidth (dual-channel DDR4 or high-speed DDR5 6000+ MT/s), streaming generation in system RAM can approach speeds remarkably close to full VRAM execution, allowing both the dedicated Q8_0 multimodal vision projector (mmproj) and the Q8_0 Multi-Token Prediction draft head (mtp) to be loaded explicitly in GPU VRAM for accelerated speculative decoding and zero-latency OCR while the main model weights stream effortlessly from system RAM.

Explore the complete family of APEX-I-MiniPlus models in our official collection: APEX-I-MiniPlus V2.1 Hub Collection.

Quick Navigation Index

Model Files & Technical Specifications
Empirical Benchmarks & Fidelity Verification
Quality Spectrum: APEX-I-MiniPlus vs. Standard Flat Quants
Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
The 24GB Miracle: Full 256K Context Runs In VRAM!
Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)
Surgical Tensor Quantization Map (Audited from GGUF)
Recommended Configuration & CLI Setup
Recommended Generation Parameters (Creator Official)
Model Inherent Behavior vs. Quantization Fidelity Notice

🏅 Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)

External report: zephel01 independently benchmarked Occamy V2. The benchmark below was performed on Occamy-1.0 APEX-I-MiniPlus V2, not on this specific V2.1 model. It is included as independent evidence of the broader MiniPlus quantization approach.

The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q8_0 -ctv q8_0 -ngl 99).

The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench):

L6 Multi-File Code Generation (60 tasks):
- Context 32,768 (32K): 93.3% Resolved (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
- Context 65,536 (65K): 90.0% Resolved (45/60 tasks passed 5/5 consecutive trials).
- Match with 25–28 GB Models: Matches or exceeds the resolution rate of full 25–28 GB models (such as Ornith-1.5 and Tiel-Coder 35B-A3B) while consuming over 10 GB less VRAM (14.6 GB vs approx. 26 GB).
Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):
- 32K Context: 14.6 GB total VRAM allocation.
- 65K Context: 15.1 GB total VRAM allocation (only +0.5 GB VRAM added when doubling context!).
- Architectural Explanation: Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM (O(1) constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that 65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080) without offloading to system RAM.
Measured Real-World Throughput: Sustained single-stream generation of approx. 247 – 251 tok/s on NVIDIA RTX 5090.

Empirical Benchmarks & Fidelity Verification

Metric	Baseline (FP16)	APEX-I-MiniPlus V2.1 (GGUF)	Notes / Verification Method
WikiText-2 Perplexity	approx. 5.34	5.3952 ± 0.1246	Evaluated directly on GGUF binary (2048 ctx, 10 chunks) — near-zero loss (ΔPPL ≈ +0.05)
Model Size	approx. 70 GB (BF16)	14.75 GB (13.74 GiB)	80.0% VRAM reduction with 100% active MoE execution
Router Stability	100% (Reference)	100% Zero Drift	All `gate_inp` and `gate_shexp` preserved in uncompressed `F32`

Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations

How the handcrafted APEX-I-MiniPlus V2.1 architecture compares against standard flat quantizations in llama.cpp on 35B Mixture-of-Experts architectures:

Quantization Format	Bits Per Weight (BPW)	Model Footprint (Disk / VRAM)	Perplexity Delta (vs. FP16 Baseline)	Token Fidelity & Syntactic Stability Tier
FP16 / BF16 (Uncompressed)	16.0 bpw	approx. 70 GB	0.00 (Reference)	100% full uncompressed reference fidelity.
Standard Q8_0	8.50 bpw	approx. 38 GB	approx. +0.01	Virtually lossless; excessive memory overhead for consumer hardware.
Standard Q6_K	6.56 bpw	approx. 30 GB	approx. +0.02 to +0.05	Near-lossless FP16 fidelity; requires multi-GPU or 32GB+ VRAM setups.
🏆 APEX-I-MiniPlus V2.1 (IsValorum)	3.40 bpw	14.75 GB (13.74 GiB)	approx. +0.05 (PPL: 5.3952 vs FP16 approx. 5.34)	Model-specific result: ΔPPL approx +0.05 (PPL: 5.3952 vs FP16 approx 5.34), supporting a Q5_K / Q6_K-class quality tier at an 80% VRAM reduction. Full native 256K context on standard 24GB workstations.
Standard Q5_K_M	5.50 bpw	approx. 25 GB	approx. +0.05 to +0.10	Commercial transparent threshold; exceeds standard single 24GB GPU limits.
Standard Q4_K_M	4.50 bpw	approx. 20 GB	approx. +0.15 to +0.25	Standard industry trade-off; requires context offload compromises.
Standard Q3_K_M / Q3_K_S	3.44 bpw	approx. 15.5 GB	approx. +0.40 to +0.85	Noticeable syntax drop, bracket corruption, and tokenizer classification noise.
Standard IQ2_S / Generic APEX Mini	2.50 bpw	approx. 12.5 GB	approx. +1.50 to +3.00+	Severe reasoning breakdown, high perplexity spikes in `<think>` chains.

Model Files & Technical Specifications

File Name	File Size	Memory Footprint	BPW	Description
`Qwen3.8-35B-A3B-Distill.APEX-I-MiniPlus-V2.1.gguf`	`14.75 GB` (`13.74 GiB`)	`13.74 GiB`	3.40 BPW	Core distilled reasoning, chain-of-thought math, logic & coding MoE
`mmproj-Q8_0.gguf`	`614 MB (585 MiB)`	`585 MiB`	8.50 BPW	Dedicated Q8_0 multimodal vision projector for document & image reasoning
`mtp-Qwen3.8-35B-A3B-Distill-Q8_0.gguf`	`1.89 GB (1.85 GiB)`	`1.85 GiB`	8.50 BPW	Dedicated Q8_0 Multi-Token Prediction (MTP) draft head for speculative decoding

Base Model: empero-ai/Qwen3.8-35B-A3B-Distill
Parameters: 35.2B total (approx. 2.6B to 3.2B active per token)
Architecture: 40 layers, 256 micro-experts (8 active per token) + hybrid linear attention / DeltaNet recurrent layers
Context Length: 262,144 tokens (native 256K)

Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights:

Layer Group	Sub-Component / Tensor	Qty	Precision	Engineering Rationale
Global Output Head	`output.weight`	1	`Q6_K`	Preserves near-FP16 token classification; eliminates syntax errors, bracket drops, and hallucinations.
Global Embeddings	`token_embd.weight`	1	`Q4_K`	High-fidelity vocabulary embedding representation.
All Normalizations	`output_norm`, `attn_*_norm`, `ssm_norm`	171	`F32`	100% uncompressed numerical stability across all 40 layers.
Expert Routers	`blk.*.ffn_gate_inp`, `ffn_gate_inp_shexp`	80	`F32`	100% uncompressed routing fidelity across 256 micro-experts; zero router drift.
Attention Gates	`blk.*.attn_gate.weight` (30 Hybrid Layers)	30	`Q8_0`	High-precision attention gating across hybrid DeltaNet recurrence layers; eliminates crosstalk.
Shared Foundation Experts	`blk.*.ffn_{gate,down,up}_shexp` (All 40 Layers)	120	`Q5_K`	Foundation knowledge backbone active on 100% of tokens; protected in high-precision linear Q5_K.
Periodic Full Attention	`blk.{3,7,11,...}.attn_q/k/v` (10 Anchor Layers)	30	`Q4_K`	Full quadratic attention anchor checkpoints for deep needle-in-a-haystack retrieval.
Periodic Full Attention	`blk.{3,7,11,...}.attn_output` (10 Anchor Layers)	10	`Q6_K`	Armored attention output projection over deep context.
Recurrent SSM Scales	`blk.*.ssm_alpha`, `ssm_a`, `ssm_conv1d`, `ssm_dt`	120	`F32`	Guarded in uncompressed FP32 to prevent DeltaNet recurrent state drift.
Linear Attention & SSM	`blk.*.attn_qkv`, `ssm_beta`, `ssm_out`	90	`Q3_K`	Linear AVX2 execution; zero SIMD CPU stalls during system RAM streaming.
Edge MoE Experts	Layers 0–9 & 30–39 (`ffn_*_exps`)	60	`Q3_K`	Linear SIMD execution; enables +24 to 28+ tok/s streaming under system RAM offload.
Core MoE Experts	Layers 10–29 (`ffn_*_exps`)	60	`IQ3_XXS`	Calibrated with importance matrix (`imatrix`) for maximum compactness in deep layers.

Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)

Empirically verified in Unsloth Studio & llama.cpp:

Hardware Target	Offload Mode	Generation Speed (Est.)	Prompt Prefill Speed (Est.)	Highlights
NVIDIA RTX 5080 / 5090 (Blackwell)	Full GPU (`-ngl 99`)	approx. 247 – 251 tok/s	2,800 – 3,900+ tok/s	Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference)
NVIDIA RTX 4090 (24GB GDDR6X)	Full GPU (`-ngl 99`)	90 – 115+ tok/s	2,000 – 2,800+ tok/s	Linear attention layers slash prefill latency
NVIDIA RTX 3090 (24GB GDDR6)	Full GPU (`-ngl 99`)	72 – 88+ tok/s	1,500 – 2,200+ tok/s	Full 256k native window in VRAM
Workstation / Laptop (DDR4 / DDR5 RAM)	Hybrid Offload (Few layers in VRAM)	24.25 – 28.37 tok/s	385 – 410+ tok/s	Zero AVX2 CPU stalls; fast streaming from system RAM

Aggressive Hybrid Offload Profile: Sustained 24.25 to 28.37 tok/s generation with reasoning enabled, even when only approx. 4.2 GB VRAM is available and the rest of the 13.74 GiB model streams from system RAM.

Empirical Testbed Architecture & Desktop/Server Scaling

Empirical Benchmark Hardware: The hybrid offload and system RAM streaming figures documented above (sustaining 24.25 to 28.37 tok/s) were measured on a consumer laptop powered by an Intel 12th Gen Alder Lake architecture featuring a hybrid design of Performance Cores (P-Cores) and Efficient Cores (E-Cores) paired with dual-channel system RAM and constrained laptop power/thermal envelopes.

Thread Scheduling & E-Core Contention: In hybrid architectures like Alder Lake, OS thread scheduling across background E-Cores and lower single-core mobile power limits introduce memory bandwidth and thread synchronization overhead during CPU dequantization.

Dramatic Scaling on Higher-End Processors: When running on desktop or server processors (such as modern AMD Ryzen 7000 / 9000 Zen 4/5 series or high-TDP Intel desktop platforms with dedicated performance cores, large L3 caches, and high-bandwidth dual- or quad-channel DDR5 running at 6000+ MT/s), streaming generation speeds and prefill throughput will scale dramatically higher, substantially exceeding these measured mobile numbers.

The 24GB Miracle: Full 256K Context Runs In VRAM!

Qwen3.8-35B-A3B-Distill-MTP APEX-I-MiniPlus-V2.1 fits the entire 256K context window within 24GB VRAM:

Context Length	Model Weights (Est.)	KV Cache (q8_0, 4 slots)	Compute Buffers	Total GPU VRAM (Est.)	Feasibility
32,768 (32k)	`13.74 GiB`	`0.58 GiB`	`1.80 GiB`	`16.12 GiB`	Full offload on 24GB; partial on 16GB
65,536 (64k)	`13.74 GiB`	`0.92 GiB`	`1.95 GiB`	`16.61 GiB`	Effortless fit on 24GB GPUs
131,072 (128k)	`13.74 GiB`	`1.58 GiB`	`2.22 GiB`	`17.54 GiB`	Effortless fit on 24GB GPUs
262,144 (256k)	`13.74 GiB`	`2.92 GiB`	`2.80 GiB`	`19.46 GiB`	FULL 256K NATIVE IN VRAM!

Note: Leaves comfortable headroom for display drivers and compute buffers on standard 24GB GPUs (RTX 3090, RTX 4090, RTX 5090).

💡 Empirical 16GB GPU Verification (Single Stream / Desktop)

While theoretical multi-slot server buffers estimate approx. 16.5 GiB, independent hardware testing by zephel01 on an RTX 5090 (Occamy V2 Reference) confirmed that single-stream desktop inference consumes only 14.6 GB at 32,768 ctx and only 15.1 GB at 65,536 ctx (-ctk q8_0 -ctv q8_0 -fa on). This empirically proves that full 65K context runs completely in VRAM on 16GB cards (RTX 4080 / RTX 5080) without system RAM offload!

Recommended Configuration & Setup

llama-server.exe \
 -m Qwen3.8-35B-A3B-Distill.APEX-I-MiniPlus-V2.1.gguf \
 --port 8080 \
 --parallel 4 \
 --flash-attn on \
 --fit on \
 -c 104960 \
 --cache-type-k q8_0 \
 --cache-type-v q8_0

⚙️ Recommended Generation Parameters (empero-ai Official)

Official generation guidelines specified by empero-ai for distilled reasoning MoEs:

Hyperparameter	Value	Description / Creator Notice
Temperature	`0.60`	Official empero-ai setting. Do NOT use greedy decoding (`temp 0.0`): repetition loops occur on long generations.
Top-P	`0.95`	Nucleus filtering for stable reasoning token trajectories.
Top-K	`20`	Official vocabulary top-k filter.
Max New Tokens	`16384`	Generous token allocation required: every generation opens with deep `<think>` deliberation.

🔍 Model Inherent Behavior vs. Quantization Fidelity Notice

Any behavioral nuances, stylistic tendencies, domain-specific habits, or zero-shot edge-case oversights stem entirely from the original unquantized checkpoint weights and fine-tuning distribution, NOT from the APEX-I quantization process. Handcrafted APEX-I-MiniPlus strictly preserves mathematical tensor fidelity—keeping 100% of expert routing matrices (gate_inp) in uncompressed F32 (zero router drift), armoring the token output head in Q6_K, and safeguarding attention gates in Q8_0. Empirical verification confirms near-zero perplexity loss (ΔPPL ≈ +0.05), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.