Will It Run AI

Can Qwen3-Coder 480B A35B Instruct run on RTX PRO 6000 Blackwell Server Edition 96GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Qwen3-Coder 480B A35B Instruct needs ~306.2 GB but RTX PRO 6000 Blackwell Server Edition 96GB only has 96.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 306.2 GB, exceeds 96.0 GB available
306.2 GB required96.0 GB available
319% VRAM needed

210.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

92293 ms

Safe context

4K

Memory

306.2 GB / 96.0 GB

Offload

70%

Memory breakdown

Weights292.8 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom9.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3-Coder 480B A35B Instruct on RTX PRO 6000 Blackwell Server Edition 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 2.1 tok/s decode · 92.3s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 306.2 GB, but this setup only exposes 96.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.1 tok/s50342 ms4K
CodingFToo heavy2.0 tok/s96800 ms4K
Agentic CodingFToo heavy2.1 tok/s134245 ms4K
ReasoningFToo heavy2.1 tok/s109074 ms4K
RAGFToo heavy2.1 tok/s167806 ms4K

Quantization options

How Qwen3-Coder 480B A35B Instruct (480B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
187.2 GB
LowF0
Q3_K_S
3
235.2 GB
LowF0
NVFP4
4
268.8 GB
MediumF0
Q4_K_M
4
292.8 GB
MediumF0
Q5_K_M
5
345.6 GB
HighF0
Q6_K
6
393.6 GB
HighF0
Q8_0
8
513.6 GB
Very HighF0
F16
16
984.0 GB
MaximumF0

升级选项

能流畅运行 Qwen3-Coder 480B A35B Instruct 的硬件

AMD Instinct MI350X 288GB最佳性价比
288 GB VRAM (+192)8000 GB/s (+6403)
A
Makes the model fit on the accelerator instead of staying completely out of reach.35.3 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 1581%.

~$8,000 MSRP

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run Qwen3-Coder 480B A35B Instruct?

No, Qwen3-Coder 480B A35B Instruct requires more memory than RTX PRO 6000 Blackwell Server Edition 96GB provides.

How much VRAM does Qwen3-Coder 480B A35B Instruct need?

Qwen3-Coder 480B A35B Instruct (480B parameters) requires approximately 306.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 480B A35B Instruct?

The recommended quantization for Qwen3-Coder 480B A35B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 480B A35B Instruct run at on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen3-Coder 480B A35B Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run Qwen3-Coder 480B A35B Instruct for coding?

For coding workloads, Qwen3-Coder 480B A35B Instruct on RTX PRO 6000 Blackwell Server Edition 96GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Qwen3-Coder 480B A35B Instruct use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen3-Coder 480B A35B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder 480B A35B Instruct feels slow on RTX PRO 6000 Blackwell Server Edition 96GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX PRO 6000 Blackwell Server Edition 96GBSee all hardware for Qwen3-Coder 480B A35B Instruct
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