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Best GPU for QWEN-3.6-35B-A3B Locally

Real-time prices and hardware recommendations updated for July 2026.

35B
3B
moe

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Budget EntryQ3_K_M quant
15.1 GB
0.4 GB
Total VRAM:15.5 GB

Recommended Hardware

Radeon RX 9060 XT 16GB16GB VRAM
1444 tok/sprefill
59 tok/sgeneration
JPY 71800View Card
GeForce RTX 5060 Ti 16GB16GB VRAM
2300 tok/sprefill
98 tok/sgeneration
JPY 102639View Card

MoE model weights require 16GB VRAM minimum for Q3 quantizations.

Balanced Sweet SpotQ4_K_M quant
20.1 GB
0.4 GB
Total VRAM:20.5 GB

Recommended Hardware

GeForce RTX 309024GB VRAM
3320 tok/sprefill
105 tok/sgeneration
JPY 370822View Card
RTX A500024GB VRAM
1409 tok/sprefill
86 tok/sgeneration
JPY 501387View Card
GeForce RTX 409024GB VRAM
4516 tok/sprefill
154 tok/sgeneration
JPY 745492View Card
RTX PRO 4000 Blackwell24GB VRAM
3373 tok/sprefill
121 tok/sgeneration
Out of StockView Card

Runs completely inside 24GB framebuffers. High active speed due to MoE execution path.

Near LosslessQ8_0 quant
40.3 GB
0.4 GB
Total VRAM:40.6 GB

Recommended Hardware

Radeon PRO W790048GB VRAM
3106 tok/sprefill
57 tok/sgeneration
JPY 847220View Card
RTX A600048GB VRAM
1961 tok/sprefill
51 tok/sgeneration
JPY 1198799View Card
RTX PRO 5000 Blackwell48GB VRAM
5295 tok/sprefill
143 tok/sgeneration
Out of StockView Card

Requires 48GB+ workstation cards to hold the full Q8 weight matrix comfortably.

Optimizing Setup for QWEN-3.6-35B-A3B

Quantization Recommendations

For daily coding and reasoning tasks, Q4_K_M (4-bit quantization) offers the best balance of quality and memory efficiency — it reduces memory requirements by over 70% with minimal quality loss compared to FP16. Q8 and higher presets preserve more fidelity at the cost of significantly higher VRAM usage, which may force layer offloading and hurt throughput.

Recommended Local Software

We recommend using Ollama as the primary runner for local inference due to its automated GPU model splitting and context cache optimizations. For advanced fine-tuning or quantization splits, llama.cpp with Flash Attention compiled natively provides the best granular control.

How token speeds are estimated

Two metrics are shown per GPU: Read tok/s (how fast the model ingests your prompt) and Decode tok/s (how fast it streams tokens back). They model fundamentally different bottlenecks.

📖

Read (Prefill)

The prompt is processed in one parallel pass. This is compute-bound: it saturates the GPU's tensor cores.

read tok/s ≈ TFLOPS × readFactor × 400 ÷ activeParams

Decode (Generation)

Each new token requires loading the entire model's active weights from VRAM. This is memory-bandwidth-bound: the GPU stalls waiting for data, not computing.

decode tok/s ≈ bandwidth × decodeFactor ÷ (weights + kv_cache)

Weights = (activeParams × bits ÷ 8) × 1.15 overhead. KV cache per step = activeParams × multiplier × contextK.

Architecture utilization factors

Architecture
Decode
Read
Blackwell, Xe2
0.45
0.55
Ada Lovelace, RDNA 4, Battlemage
0.38
0.48
Ampere, Turing, RDNA 3, Xe-HPG
0.28
0.38
Volta, RDNA 1/2
0.2
0.25
Pre-tensor-core (Pascal, Maxwell, Kepler, GCN, Alchemist)
0.12
0.15

Left: decode factor — Right: read factor

Data sources

TFLOPS and memory bandwidth are read from the GPU database. When missing, bandwidth falls back to a hardcoded dictionary.

Limitations

These are analytical estimates, not benchmark results. Use them as a relative comparison, not an absolute performance guarantee.