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+---
+created: '2026-04-09T18:00:31+00:00'
+id: 587014e6-a793-4f79-af13-e29a19e7d777
+keywords:
+- Qwen3-Coder-Next
+- Alibaba
+- coding model
+- temperature
+- top_p
+- top_k
+- min_p
+- GGUF
+- quantization
+- UD-Q4_K_XL
+- FP8
+- vLLM
+- llama.cpp
+- llama-server
+- MoE
+- 80B
+- 3B active
+- non-reasoning
+- agentic coding
+- SWE-Bench
+- tool calling
+links: []
+modified: '2026-04-09T18:00:31+00:00'
+summary: Optimal running parameters for Qwen3-Coder-Next (80B MoE, 3B active), covering
+  inference settings, hardware requirements, quantization benchmarks, FP8 vLLM deployment,
+  tool calling setup, and llama.cpp example commands.
+tags:
+- reference
+- qwen3-coder-next
+- llm
+- inference
+- llama-cpp
+- local-ai
+- coding
+title: Qwen3-Coder-Next — Optimal Running Parameters
+---
+
+# Qwen3-Coder-Next — Optimal Running Parameters
+
+A reference for optimally running Qwen3-Coder-Next, Alibaba's 80B MoE (3B active parameters) coding model, locally.
+
+## Model Overview
+
+| Property | Value |
+|---|---|
+| Architecture | MoE — 80B total, **3B active parameters** |
+| Max Context | **262,144** (256K) natively |
+| Recommended Context | **32,768** for less memory use |
+| Thinking Mode | **Non-reasoning only** — no thinking/reasoning mode. Does not generate `<think)>` blocks. |
+| Best For | Fast agentic coding, long-horizon reasoning, complex tool use, recovery from execution failures |
+| Performance Tier | Comparable to models with 10–20× more active parameters |
+
+## Recommended Inference Parameters
+
+These are Qwen's recommended settings for optimal performance:
+
+| Parameter | Value | Notes |
+|---|---|---|
+| `temperature` | **1.0** | |
+| `top_p` | **0.95** | |
+| `top_k` | **40** | |
+| `min_p` | **0.01** | llama.cpp default is 0.05 — override to 0.01 |
+| `repetition_penalty` | **1.0** (disabled) | Only increase if you see looping |
+
+### Quick Reference — One-Liner
+
+```
+temperature=1.0, top_p=0.95, top_k=40, min_p=0.01, repetition_penalty=1.0
+```
+
+## Hardware Requirements (Total Memory: RAM + VRAM, or Unified Memory)
+
+| Precision | Memory Required |
+|---|---|
+| 3-bit | ~34 GB |
+| **4-bit (UD-Q4_K_XL)** | **~46 GB** |
+| 8-bit | ~85 GB |
+| BF16 | ~160 GB |
+
+> **Rule of thumb:** `disk space + RAM + VRAM ≥ size of quant`. If the quant fully fits on your device, expect **20+ tokens/s**. If it doesn't fit, it'll still work via offloading but will be slower.
+
+### Recommended Minimum Quantization
+
+- **3-bit (`UD-IQ3_XXS`)** — comes close to BF16 performance; sensible minimum for most use cases.
+- **4-bit (`UD-Q4_K_XL`)** — recommended starting point; best balance of size and quality.
+- At least **Q4_K_M** is advised based on third-party benchmarks (Unsloth's Q4_K_M outperforms standard Q4_K_M).
+
+## Thinking Mode
+
+This model is **non-reasoning only**. It does NOT support thinking/reasoning mode and does not generate `<think)>` blocks.
+
+- `enable_thinking=False` is **not required** — it's the only mode.
+- This design choice enables **ultra-quick code responses**.
+
+## Critical Warnings
+
+- ⚠️ **Update llama.cpp** — a previous bug in `vectorized key_gdiff` calculation caused looping and output issues. Re-download GGUFs and update llama.cpp.
+- ⚠️ **No Ollama support** — use llama.cpp-compatible backends.
+- ⚠️ If context length is set too low, you may see errors like `exceeds the available context size`. Increase `--ctx-size` or use `--fit on` for auto-detection.
+- ⚠️ Tool-calling improved after llama.cpp parsing fixes (Feb 19 update). Ensure you're on a recent version.
+
+## Example llama.cpp Commands
+
+### Direct Pull from Hugging Face (4-bit)
+
+```bash
+./llama.cpp/llama-cli \
+    -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL \
+    --ctx-size 16384 \
+    --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40
+```
+
+### Conversation Mode with Local GGUF
+
+```bash
+./llama.cpp/llama-cli \
+    --model unsloth/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-UD-Q4_K_XL.gguf \
+    --seed 3407 \
+    --temp 1.0 \
+    --top-p 0.95 \
+    --min-p 0.01 \
+    --top-k 40
+```
+
+### llama-server Deployment
+
+```bash
+./llama.cpp/llama-server \
+    --model unsloth/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-UD-Q4_K_XL.gguf \
+    --alias "unsloth/Qwen3-Coder-Next" \
+    --seed 3407 \
+    --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40 \
+    --port 8001
+```
+
+### OpenAI-Compatible Client
+
+After deploying via `llama-server`:
+
+```python
+from openai import OpenAI
+
+openai_client = OpenAI(
+    base_url = "http://127.0.0.1:8001/v1",
+    api_key = "sk-no-key-required",
+)
+
+completion = openai_client.chat.completions.create(
+    model = "unsloth/Qwen3-Coder-Next",
+    messages = [{"role": "user", "content": "Create a Flappy Bird game in HTML"}],
+)
+
+print(completion.choices[0].message.content)
+```
+
+## FP8 Dynamic Quant (vLLM)
+
+For premium and fast inference with GPU setups, use Unsloth's [FP8 Dynamic quant](https://huggingface.co/unsloth/Qwen3-Coder-Next-FP8-Dynamic). FP8 can boost throughput by **25% or more**.
+
+### Install vLLM Nightly
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv venv unsloth_fp8 --python 3.12 --seed
+source unsloth_fp8/bin/activate
+
+uv pip install --upgrade --force-reinstall vllm --torch-backend=auto \
+    --extra-index-url https://wheels.vllm.ai/nightly/cu130
+uv pip install --upgrade --force-reinstall git+https://github.com/huggingface/transformers.git
+uv pip install --force-reinstall numba
+```
+
+> Change `cu130` to your CUDA version (only `cu129` and `cu130` currently supported). Check via `nvidia-smi`.
+
+### Serve FP8 with vLLM (Multi-GPU)
+
+```bash
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False
+CUDA_VISIBLE_DEVICES='0,1,2,3' vllm serve unsloth/Qwen3-Coder-Next-FP8-Dynamic \
+    --served-model-name unsloth/Qwen3-Coder-Next \
+    --tensor-parallel-size 4 \
+    --tool-call-parser qwen3_coder \
+    --enable-auto-tool-choice \
+    --dtype bfloat16 \
+    --seed 3407 \
+    --max-model-len 200000 \
+    --gpu-memory-utilization 0.93 \
+    --port 8001
+```
+
+> - For single GPU: `CUDA_VISIBLE_DEVICES='0'` and `--tensor-parallel-size 1` (or remove the flag).
+> - Add `--kv-cache-dtype fp8` to reduce KV cache memory usage by **50%**.
+
+## Quantization Benchmarks (Third-Party)
+
+### Aider Polyglot Benchmarks
+
+- **3-bit (`UD-IQ3_XXS`)** comes close to **BF16** performance — sensible minimum.
+- Quality steadily improves: **1-bit → 2-bit → 3-bit → 6-bit**.
+- **Unsloth's Q4_K_M** outperforms standard (non-Unsloth) Q4_K_M.
+- **At least Q4_K_M is advised.**
+
+### Benjamin Marie Benchmarks (750-prompt suite: LiveCodeBench v6, MMLU Pro, GPQA, Math500)
+
+- Unsloth's Q4_K_M quants perform better than standard Q4_K_M.
+- Q3_K_M performs worse on LiveCodeBench v6 but surprisingly better on HumanEval than standard Q4_K_M.
+
+## Performance Comparison
+
+Qwen3-Coder-Next (80B, 3B active) vs. much larger models:
+
+| Benchmark | Qwen3-Coder-Next (80B) | DeepSeek-V3.2 (671B) | GLM-4.7 (358B) | MiniMax M2.1 (229B) |
+|---|---|---|---|---|
+| SWE-Bench Verified | 70.6 | 70.2 | 74.2 | 74.8 |
+| SWE-Bench Multilingual | 62.8 | 62.3 | 63.7 | 66.2 |
+| SWE-Bench Pro | **44.3** | 40.9 | 40.6 | 34.6 |
+| Terminal-Bench 2.0 | 36.2 | 39.3 | 37.1 | 32.6 |
+| Aider | 66.2 | 69.9 | 52.1 | 61.0 |
+
+## Sources
+
+- Unsloth Qwen3-Coder-Next Documentation (How to Run Locally)
+- GGUF collection: <https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF>
+- FP8 Dynamic: <https://huggingface.co/unsloth/Qwen3-Coder-Next-FP8-Dynamic>
+- Third-party benchmarks: Aider Polyglot, Benjamin Marie
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