Qwen3-Coder 480B-A35B has 480.1B parameters (35B active per token). In BF16 its weights alone take 894 GiB; quantized to 4-bit, about 270 GiB. Each 32K-token request adds 7.75 GiB of KV cache. Cheapest way to run it today: 7 × RTX 6000 Ada (48 GB) at $3.46 per hour.
Read every day from Hugging Face. Split files are added together; vision projectors are left out.
How many GPUs to run Qwen3-Coder 480B-A35B
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
GPU
BF16 / FP16
FP8 / INT8
4-bit (GGUF Q4_K_M)
GeForce RTX 4090 24 GB
48 (several servers)
24 (several servers)
16 (several servers)
GeForce RTX 5090 32 GB
48 (several servers)
24 (several servers)
16 (several servers)
RTX PRO 6000 Blackwell 96 GB
16 (several servers)
6
4
Mac M5 Max 128 GB 96 GB usable
16 (several servers)
6
4
NVIDIA H100 80 GB
16 (several servers)
7
4
NVIDIA H200 141 GB
8
4
3
NVIDIA B200 (HGX) 180 GB
6
3
2
Cheapest way to run Qwen3-Coder 480B-A35B today
For each GPU: the fewest cards that fit Qwen3-Coder 480B-A35B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-10).
On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
KV cache: how memory grows with context
Architecture: MoE 160 experts · 62 layers, full attention (GQA, 8 KV heads × 128). Every layer keeps keys and values for every token (grouped-query attention), so the cache grows linearly with context. Only 35B parameters are active per token, but all 480B must sit in memory: VRAM depends on the total, speed on the active part.
Context per request
BF16 cache
FP8 cache
8K tokens
1.94 GiB
0.97 GiB
32K tokens
7.75 GiB
3.88 GiB
128K tokens
31.0 GiB
15.5 GiB
256K tokens
62.0 GiB
31.0 GiB
Qwen3-Coder 480B-A35B VRAM FAQ
How much VRAM does Qwen3-Coder 480B-A35B need?
In BF16 the weights alone take 894 GiB (960 GB). In FP8 that is 449 GiB, and about 270 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 1.94 GiB at 8K tokens and 7.75 GiB at 32K (BF16 cache).
Can Qwen3-Coder 480B-A35B run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 16 (several servers) RTX 4090s. In 4-bit it needs 4 H100 GPUs.
What is the cheapest way to run Qwen3-Coder 480B-A35B?
On 2026-10-10, the cheapest on-demand setup is 7 × RTX 6000 Ada (48 GB) with 4-bit (GGUF Q4_K_M) weights on Vast.ai, at $3.46 per hour (about $2,524.34 per month). Next: 4 × NVIDIA A100 80GB with 4-bit (GGUF Q4_K_M) weights on Vast.ai, at $3.74 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Qwen3-Coder 480B-A35B need?
With one request and an 8K-token context: 16 (several servers) in BF16, 7 in FP8 and 4 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Qwen3-Coder 480B-A35B VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/qwen3-coder-480b-a35b.json.
Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.