local-ai/docs/gpu-setup-research.md

# GPU Setup Research: Rack Server AI Workloads

*Last updated: March 22, 2026*

## Goal
Cost-efficient rack-mountable GPU setup for:
1. **LLM coding inference** — Run 32B+ parameter coding models with maximum context windows
2. **Image generation** — ComfyUI / InvokeAI with Stable Diffusion SDXL / Flux

Target servers: Dell R720/R730 or HP DL380 equivalent (2U rack)

## Why 48GB VRAM is the Right Target

### The Problem with 24GB
32B coding models at Q4_K_M quantization use ~20GB of weights, leaving only ~4GB for KV cache
on a 24GB card. This severely limits context window size — the key ingredient for complex
coding sessions where the model needs to understand your entire codebase.

### What 48GB Unlocks
- **32B models at higher quantization** (Q6_K/Q8_0) = better output quality
- **28GB+ free for KV cache** = massive context windows (32K+ tokens)
- **70B models** in aggressive quantization (~12 t/s but functional)
- **Simultaneous model loading** — coding model + image gen model at once
- Room for future larger models without hardware changes

## Best Local Coding Models (2026)

### Qwen 3.5 Family (February 2026 — Gated Delta Networks)

Architecture breakthrough: 3 of every 4 layers use **linear attention** (O(n) scaling),
drastically reducing KV cache memory. These models need far less VRAM for long contexts
than traditional transformers.

| Model | Type | Active Params | Size at Q4_K_M | Max Context | Quality | Notes |
|-------|------|---------------|---------------|-------------|---------|-------|
| **Qwen3.5-35B-A3B** | **MoE** | **3B** | **~12GB** | **262K** | **A-** | Best bang for buck — 35B model, 3B active, fits 262K ctx in 25GB |
| **Qwen3.5-27B** | Dense | 27B | ~17GB | 262K | A- | 72.4% SWE-bench, ties GPT-5 mini |
| **Qwen3.5-122B-A10B** | MoE | 10B | ~76GB | 262K | A | Matches GPT-5 mini across the board |
| **Qwen3.5-9B** | Dense | 9B | ~6GB | 262K | B+ | Fits on any modern GPU |
| **Qwen3.5-4B** | Dense | 4B | ~3GB | 262K | B | Tiny but capable |

### Previous Generation (Still Relevant)

| Model | Size at Q4_K_M | Quality | Notes |
|-------|---------------|---------|-------|
| **Qwen2.5-Coder 32B** | ~20GB | 73.7 Aider (≈ GPT-4o) | FIM king, 92.7% HumanEval |
| **Qwen3-Coder 30B-A3B** (MoE) | ~18GB | #1 SWE-rebench (64.6%) | Only 3.3B active, very fast |
| **Qwen3-Coder-Next 80B** (MoE) | needs 64GB+ RAM offload | Beats Claude Opus 4.6 on SWE-rebench | Hybrid attention, 256K context |

### Honest Assessment: Local vs Claude Code
Nothing local approaches Claude Opus 4.6 quality for complex multi-file agentic coding.
These 32B models are competitive with **GPT-4o** — a tier below Claude Sonnet, two tiers below Opus.
Best strategy: use local models for routine tasks, save Claude credits for hard problems.

## 48GB GPU Market (March 22, 2026 — Real Prices)

| GPU | Arch | Used Price | TDP | Cooling | Tensor Cores | Mem BW |
|-----|------|------------|-----|---------|--------------|--------|
| **Quadro RTX 8000** | Turing (2018) | **$2,000–2,900** | 260W | Passive variant | Yes (576) | 672 GB/s |
| **A40** | Ampere (2020) | **~$5,050+** | 300W | Passive | Yes (336 3rd-gen) | 696 GB/s |
| **RTX A6000** | Ampere (2020) | **~$5,400+** | 300W | Active (blower) | Yes (336 3rd-gen) | 768 GB/s |
| **L40** | Ada (2022) | **~$6,500+** | 300W | Passive | Yes (568 4th-gen) | 864 GB/s |
| **RTX 6000 Ada** | Ada (2022) | **~$6,500+** | 300W | Active | Yes (568 4th-gen) | 960 GB/s |

Sources: eBay active/sold listings, GPUPoet price tracking, Pangoly, CamelCamelCamel (all March 2026)
Note: One outlier RTX 8000 listing at ~$750 exists but is not representative of the market.

### Cheapest 48GB Option: Quadro RTX 8000 Passive ($2,000–2,900)

The RTX 8000 is still the cheapest 48GB card — roughly half the price of an A40 and a
third of an A6000. The passive variant is purpose-built for rack servers — no fan, relies
on chassis airflow, designed for 24/7 operation in 2U/4U systems.

Key advantages over the P40:
- **48GB vs 24GB** — room for models + massive context
- **Has Tensor Cores** (576 Turing) — native FP16, no `--force-fp32` hacks for image gen
- **NVLink support** — pair two for 96GB combined (100 GB/s bidirectional)
- 10W idle power draw

### Cost Reality Check
At $2,000–2,900 the RTX 8000 is a significant investment. The key question: is unified
48GB VRAM worth 4–6x the cost of dual P40s ($400–500)?

**Yes, if** you need large context windows (32K+) for complex coding — KV cache can't
be split across two GPUs without NVLink (which P40s don't have).

**No, if** you're mostly doing short-prompt coding tasks and image gen — dual P40s give
you 48GB total (split) at a fraction of the cost, and each card can handle its own workload.

## RTX 8000 Performance Benchmarks

### LLM Inference (Exllama, 5.0 bpw quantization)

| Model | Context | Prompt Processing | Generation |
|-------|---------|-------------------|------------|
| Qwen3 30B-A3B (MoE) | 8K | 950 t/s | **34 t/s** |
| Qwen3 30B-A3B (MoE) | 16K | 673 t/s | **21 t/s** |
| Qwen3 30B-A3B (MoE) | 32K | 345 t/s | **11 t/s** |
| Llama 3.3 70B | short | 36 t/s | **13 t/s** |
| Llama 3.1 8B | — | — | **72 t/s** |

### Compared to P40 (24GB)

| Metric | P40 (24GB) | RTX 8000 (48GB) |
|--------|-----------|-----------------|
| **Used price** | **$150–320** | **$2,000–2,900** |
| 32B model fit | Barely (~2GB free) | Comfortable (~28GB free) |
| 32B generation speed | ~5-12 t/s (est.) | ~20-34 t/s |
| Max practical context | ~4K tokens | **32K+ tokens** |
| Image gen (SDXL) | ~49s (`--force-fp32`) | Faster (native FP16) |
| Rack server ready | Yes (passive) | Yes (passive variant) |

### Image Generation
The RTX 8000 has Turing Tensor Cores with native FP16 support. Unlike the P40, it does NOT
need `--force-fp32` workarounds. Image gen performance is significantly better than the P40,
though still behind Ampere/Ada cards.

## Budget Build: 2x Quadro RTX 5000 + NVLink ($850 Total)

*The best price-to-capability ratio for local AI coding in 2026.*

### Why This Works Now

Qwen 3.5 (February 2026) introduced **Gated Delta Networks** — 3 out of 4 layers use linear
attention (O(n) scaling) instead of quadratic. KV cache memory usage is dramatically lower
than traditional transformers. A 35B MoE model with 262K context now fits in ~25GB VRAM.

### Hardware

#### GPU: NVIDIA Quadro RTX 5000 (Turing, TU104)

| Spec | Value |
|------|-------|
| VRAM | 16GB GDDR6 |
| CUDA Cores | 3072 |
| Tensor Cores | 384 (Gen 2, FP16) |
| TDP | ~230W |
| NVLink | **Yes — 50 GB/s bidirectional** |
| Form Factor | Dual-slot, blower cooler (rack-friendly) |
| PCIe | 3.0 x16 |
| Used Price | **~$400** |
| Part Number | VCQRTX5000-PB |

#### NVLink Bridge (CRITICAL: RTX 5000 uses a unique smaller connector)

The Quadro RTX 5000 has a **shorter NVLink connector** than all other Quadro RTX cards.
Bridges from the RTX 6000/8000 will NOT physically fit. You must buy the RTX 5000-specific bridge.

| Detail | Value |
|--------|-------|
| Product | NVIDIA Quadro RTX 5000 NVLink HB Bridge 2-Slot |
| SKU | NVLINKX8-2SLOT-PB |
| Part Numbers | 1JF3K, 699-54934-0500-000, 900-54934-0100-000, P4934, 6FY12AA, L55997-001 |
| Price | **~$30-80** (eBay, Amazon) |
| Bandwidth | 50 GB/s total (25 GB/s per direction) |
| Sizing | 2-slot (cards adjacent) or 3-slot (one slot gap — better thermals) |

**Where to buy:**
- eBay: search "Quadro RTX 5000 NVLink" or part numbers P4934 / L55997-001 / 1JF3K
- Amazon: search part number 6FY12AA or 1JF3K

**WARNING:** The 3-slot bridge is recommended over 2-slot. With a 2-slot bridge the cards
sit directly adjacent — the top card's blower intake gets blocked by the bottom card.
A 3-slot bridge leaves an air gap for proper cooling.

#### Motherboard Requirements

| Requirement | Details |
|-------------|---------|
| PCIe slots | Two x16 slots (x8 electrical is fine — LLM inference is VRAM-bound, not PCIe-bound) |
| Slot spacing | Must match your NVLink bridge size (2-slot or 3-slot gap) |
| Power supply | 650W+ minimum (80 PLUS Gold recommended), 850W+ for headroom |
| Power connectors | 2x 8-pin PCIe power (one per card). Do NOT daisy-chain — use separate cables |
| CPU platform | Any modern platform works. Threadripper/Xeon not required |

**Recommended motherboards (workstation/server):**
- Any board with 2x PCIe x16 slots spaced 2-3 slots apart
- Server: Dell R730/R740 with GPU riser (but verify 3-slot bridge clearance in 2U)
- Workstation: MSI X399 Creation, ASUS WS series, Supermicro X11/X12 boards
- Desktop: Most ATX boards with 2 full-length x16 slots work

**Rack server note:** The Quadro RTX 5000's blower cooler exhausts out the bracket —
this works well in rack airflow. If using a 2U server, measure clearance for the NVLink
bridge sitting on top of the cards. A 4U chassis gives the most room.

### What Runs on 32GB Unified (2x RTX 5000 + NVLink)

| Model | Arch | Quant | Weights | Context | Total VRAM | Quality |
|-------|------|-------|---------|---------|------------|---------|
| **Qwen3.5-35B-A3B** | **MoE (3B active)** | **Q4_K_M** | **~12GB** | **262K** | **~25GB** | **A-** |
| Qwen3.5-27B | Dense | Q4_K_M | ~17GB | 128K+ | ~25GB | A- |
| Qwen3-Coder-Next (80B/3B active) | MoE | Q4 | ~20GB | 128K | ~28GB | A |
| Qwen2.5-Coder-14B | Dense | Q4_K_M | ~10GB | 128K | ~22GB | B+ |
| Qwen2.5-Coder-14B | Dense | Q8 | ~16GB | 64K | ~28GB | A- |
| Qwen2.5-Coder-32B | Dense | Q4_K_M | ~20GB | 16-24K | ~28GB | A- |

**The sweet spot: Qwen3.5-35B-A3B at Q4_K_M with 262K context.** This is a 35B parameter
model with only 3B active at inference (MoE). The Gated Delta Network architecture slashes
KV cache memory. The entire model + full 262K context fits in ~25GB — well within 32GB.

### What Runs on 16GB (Single RTX 5000 — Phase 1)

| Model | Quant | Context | Quality |
|-------|-------|---------|---------|
| **Qwen3.5-35B-A3B** | Q4_K_L | ~64-128K | **A-** |
| Qwen3.5-9B | Q8 | 128K+ | B+ |
| Qwen3.5-4B | Q8 | 262K | B |
| Qwen2.5-Coder-7B | Q8 | 128K | B |
| Qwen2.5-Coder-14B | Q4_K_M | 16-32K | B+ |

Even a single card can run the Qwen3.5-35B-A3B MoE model — just with a smaller context window.

### Estimated Inference Speed

| Model | 1x RTX 5000 | 2x RTX 5000 (NVLink) |
|-------|-------------|---------------------|
| Qwen3.5-35B-A3B Q4 (short ctx) | ~25-35 tok/s | ~25-35 tok/s |
| Qwen3.5-35B-A3B Q4 (128K ctx) | ~10-18 tok/s | ~15-25 tok/s |
| Qwen3.5-35B-A3B Q4 (262K ctx) | Won't fit | ~10-18 tok/s |
| Qwen2.5-Coder-14B Q4 | ~20-30 tok/s | ~25-35 tok/s |

NVLink matters most at large context windows where KV cache spans both cards.
At short contexts that fit on one card, the second GPU adds less benefit.

### Power Consumption & Cost

| Config | Idle | Load | Monthly (8hr/day @ $0.09/kWh) | Annual |
|--------|------|------|-------------------------------|--------|
| 1x Quadro RTX 5000 | ~15W | ~210W | **~$4.50** | ~$54 |
| 2x Quadro RTX 5000 | ~30W | ~420W | **~$9.00** | ~$108 |

### Software Setup

#### llama.cpp (Recommended — Best Multi-GPU Support)

```bash
# Build with CUDA support
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j$(nproc)

# Download Qwen3.5-35B-A3B GGUF (Q4_K_M)
# Get from: https://huggingface.co/unsloth/Qwen3.5-35B-A3B-GGUF

# Run on dual GPU with NVLink
./build/bin/llama-server \
  -m Qwen3.5-35B-A3B-Q4_K_M.gguf \
  -ngl 999 \
  -c 262144 \
  --host 0.0.0.0 \
  --port 8080

# llama.cpp auto-detects NVLink and splits layers across both GPUs
# Use -ts 1,1 to manually set equal split if needed
```

#### Ollama

```bash
# Requires Ollama v0.17+ for Qwen3.5 support
# NOTE: As of March 2026, some Qwen3.5 GGUFs have compatibility issues
# with Ollama due to mmproj vision files. llama.cpp may be more reliable.

# Environment variables for multi-GPU
export OLLAMA_GPU_SPLIT=16,16        # Equal split across both 16GB cards
export OLLAMA_KV_CACHE_TYPE=q8_0     # Halves KV cache VRAM with minimal quality loss
export OLLAMA_KEEP_ALIVE=24h         # Keep model loaded in VRAM
export OLLAMA_FLASH_ATTENTION=1      # Enable flash attention for VRAM savings

# Pull and run
ollama pull qwen3.5:35b-a3b-q4_K_M
ollama run qwen3.5:35b-a3b-q4_K_M
```

#### Verify NVLink Is Working

```bash
# Check NVLink status
nvidia-smi nvlink --status

# Check NVLink bandwidth
nvidia-smi nvlink -gt d

# Monitor both GPUs during inference
watch -n 0.5 nvidia-smi
```

### Total Cost Summary

| Item | Cost |
|------|------|
| 1x Quadro RTX 5000 (Phase 1) | $400 |
| 1x Quadro RTX 5000 (Phase 2) | $400 |
| NVLink HB Bridge 3-slot | ~$50 |
| **Hardware total** | **$850** |
| Monthly power (2 cards, 8hr/day) | $9/mo |
| Claude Pro subscription | $20/mo |
| **Monthly operating cost** | **~$29/mo** |
| **3-year total cost of ownership** | **$850 + $1,044 = $1,894** |

### Comparison: This Build vs Alternatives

| Setup | Cost (3yr) | Best Model | Max Context | Quality |
|-------|-----------|------------|-------------|---------|
| **2x RTX 5000 + $20 Pro** | **$1,894** | Qwen3.5-35B-A3B + Opus | 262K local | **A- local, A+ cloud** |
| 1x RTX 3090 + $20 Pro | $1,420 | Qwen3.5-35B-A3B + Opus | ~128K local | A- local, A+ cloud |
| RTX 8000 (48GB) + $20 Pro | $3,220+ | Qwen3.5-35B-A3B + Opus | 262K+ local | A- local, A+ cloud |
| Claude Max only (no GPU) | $3,600 | Opus 4.6 | 200K | A+ cloud only |
| API-only (Opus heavy use) | $18,000+ | Opus 4.6 | 200K | A+ cloud only |

### Phased Build Plan

**Phase 1 — Start with one card ($400)**
1. Buy Quadro RTX 5000 (VCQRTX5000-PB) — ~$400 on eBay
2. Install in any PCIe x16 slot
3. Install llama.cpp or Ollama v0.17+
4. Run Qwen3.5-35B-A3B at Q4_K_L with 64-128K context
5. Already A- quality for coding — test if local inference fits your workflow

**Phase 2 — Add second card + NVLink ($450)**
1. Buy matching Quadro RTX 5000 — ~$400
2. Buy NVLink HB Bridge 3-slot (part: P4934 / 1JF3K / 6FY12AA) — ~$50
3. Install second card in adjacent/nearby x16 slot
4. Connect NVLink bridge
5. Verify with `nvidia-smi nvlink --status`
6. Now running 32GB unified — Qwen3.5-35B-A3B at Q4_K_M with full 262K context

## 24GB GPU Options (Previous Research — Still Valid for Tighter Budgets)

| GPU | VRAM | Price Range | Best Deals | Notes |
|-----|------|-------------|------------|-------|
| **Tesla P40** | 24GB | $150-320 | Newegg refurb $219-270; eBay used $150-200 | Best VRAM/$ at 24GB |
| **RTX A2000 12GB** | 12GB | $250-535 | eBay used ~$250-350; one listing at $490 | Can't run 32B models |
| **Tesla T4** | 16GB | $150-350 | eBay used $150-250 | Great power efficiency |
| **RTX A4000** | 16GB | $700-750+ | eBay used ~$700; new $720+ | Too expensive for 16GB |

## Rack Server Compatibility

### Quadro RTX 8000 Passive in R720/R730
- **Physical fit**: Full-length, dual-slot — fits in GPU riser slots
- **Power**: 260W, requires 8-pin aux power + GPU enablement kit
- **Cooling**: Passive — relies on server chassis fans (same as P40)
- **Requirement**: Dual CPUs, redundant 1100W PSUs recommended
- **NVLink**: Can pair two RTX 8000s for 96GB combined VRAM
- Very similar physical/power requirements to the Tesla P40

### RTX A2000 in R720/R730
- **Physical fit**: Yes. Dual-slot, low-profile, 167mm length
- **Power**: 70W bus-powered, no aux cable needed. Must use 75W slots (slots 4-7 on R720)
- **Cooling**: Blower-style fan exhausts out bracket — ideal for rack airflow
- **Requirement**: Dual CPUs needed for GPU PCIe slots
- **Confirmed working** in Dell R740XD (similar architecture)

### Tesla P40 in R720/R730
- **Physical fit**: Yes. Full-length, single-slot, designed for rack servers
- **Power**: 250W, requires 8-pin aux power. Needs GPU enablement kit
- **Cooling**: Passive — relies on server chassis fans
- **Requirement**: Dual CPUs, redundant 1100W PSUs recommended
- **Natively supported** in these servers

### R720 vs R730
- R720: PCIe Gen2 (not a bottleneck for LLM inference, which is VRAM-bound)
- R730: PCIe Gen3, generally preferred
- Both support up to 2x double-wide or 4x single-wide GPUs

## Recommended Setups

### If budget allows ($2,000–2,900): RTX 8000 Passive
Single card handles both coding and image gen. 48GB VRAM fits 32B models with massive
context windows (32K+). Passive cooling is rack-native. Tensor cores handle FP16 image gen
properly. One card, one slot, simple setup. The premium buys you unified VRAM = big context.

### If budget allows + dedicated image gen ($2,300–3,250): RTX 8000 + A2000
RTX 8000 for coding with full 48GB dedicated to LLM context.
A2000 for image gen (3x faster than Turing, 70W, bus-powered, blower cooled).
Best separation of concerns — no model swapping needed.

### Best value ($400–500): Dual P40
Two P40s for 48GB total, but split across cards (can't combine for one model without
NVLink, which P40s lack). One for 32B coding (tight fit, ~4K context), one for image gen
(slow, needs --force-fp32). **5x cheaper than RTX 8000** but with significant context limitations.

### Cheapest entry ($200–300): Single P40
Run 32B coding model with very limited context (~4K tokens). Swap to image gen when needed.
Good for testing whether local LLM coding works for your workflow before investing more.

## Configuration Notes for local-ai stack

### For 48GB RTX 8000
```bash
# Ollama — take advantage of the full 48GB
OLLAMA_NUM_GPU=999
OLLAMA_NUM_CTX=32768  # Large context window — 48GB can handle it
OLLAMA_KEEP_ALIVE=24h

# Pull best coding models
ollama pull qwen2.5-coder:32b-instruct-q4_K_M  # ~20GB, leaves 28GB for context
ollama pull qwen3.5:27b                          # ~16GB at Q4, even more context room
ollama pull qwen3-coder:30b                      # MoE, very fast inference

# Higher quantization for better quality (48GB allows this)
# Look for Q6_K or Q8_0 variants on Ollama for better output quality
```

### For 32B models on P40 (24GB — tight fit)
```bash
OLLAMA_NUM_GPU=999
OLLAMA_NUM_CTX=4096  # Keep context small to fit in remaining VRAM
OLLAMA_KEEP_ALIVE=24h

ollama pull qwen2.5-coder:32b-instruct-q4_K_M
```

### For dual-GPU setup (RTX 8000 + A2000 or P40 + anything)
```bash
# Assign GPU 0 to Ollama (coding), GPU 1 to InvokeAI (image gen)
# In docker-compose.yml for Ollama:
CUDA_VISIBLE_DEVICES=0

# In docker-compose.yml for InvokeAI:
CUDA_VISIBLE_DEVICES=1
```

### For image gen on P40 (no tensor cores)
```bash
# InvokeAI
INVOKEAI_PRECISION=float32

# ComfyUI launch args
--force-fp32
```

### For image gen on RTX 8000 / A2000 / T4 (has tensor cores)
```bash
# InvokeAI — native FP16 works fine
INVOKEAI_PRECISION=float16

# ComfyUI — no special flags needed
```

## Sources
- [Quadro RTX 8000 for Local LLMs — Hardware Corner](https://www.hardware-corner.net/guides/quadro-rtx-8000-for-llm/)
- [RTX 8000 Passive — Network Outlet](https://networkoutlet.com/blogs/articles/nvidia-quadro-rtx-8000-48gb-passive-cooling-powering-ai-rendering-server-workloads)
- [LLM Benchmarks on Turing/Ampere GPUs — Stefandroid](https://blog.stefandroid.com/2025/06/02/benchmark-llm-performance-nvidia-gpus.html)
- [NVIDIA A40 Price Tracking — GPUPoet](https://gpupoet.com/gpu/learn/card/nvidia-a40)
- [NVIDIA L40 Price Tracking — GPUPoet](https://gpupoet.com/gpu/learn/card/nvidia-l40)
- [RTX A6000 Price History — CamelCamelCamel](https://camelcamelcamel.com/product/B09BDH8VZV)
- [RTX A6000 Price History — Pangoly](https://pangoly.com/en/price-history/pny-nvidia-quadro-rtx-a6000)
- [NVIDIA RTX A2000 Datasheet](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/rtx-a2000/nvidia-rtx-a2000-datasheet-1987439-r5.pdf)
- [Dell R730 Owner's Manual — Expansion Cards](https://www.dell.com/support/manuals/en-us/poweredge-r730/r730_ompublication/expansion-card-installation-guidelines)
- [Dell R720 Owner's Manual — Expansion Cards](https://www.dell.com/support/manuals/en-us/poweredge-r720/720720xdom/expansion-card-installation-guidelines)
- [ComfyUI GPU Benchmarks Discussion](https://github.com/Comfy-Org/ComfyUI/discussions/2970)
- [ComfyUI P40 FP32 Issue](https://github.com/Comfy-Org/ComfyUI/issues/4363)
- [Best Local LLMs for 24GB VRAM 2026](https://localllm.in/blog/best-local-llms-24gb-vram)
- [Best Coding Models 2026](https://localvram.com/en/guides/best-coding-models/)
- [Ollama VRAM Requirements Guide](https://localllm.in/blog/ollama-vram-requirements-for-local-llms)
- [Local LLMs That Can Replace Claude Code](https://agentnativedev.medium.com/local-llms-that-can-replace-claude-code-6f5b6cac93bf)
- [7 Local LLM Families to Replace Claude/Codex](https://agentnativedev.medium.com/7-local-llm-families-to-replace-claude-codex-for-everyday-tasks-25ba74c3635d)
- [Qwen2.5-Coder 32B on Ollama](https://ollama.com/library/qwen2.5-coder:32b-instruct-q4_K_M)
- [Qwen3-Coder — How to Run Locally](https://unsloth.ai/docs/models/qwen3-coder-how-to-run-locally)