Can Qwen3-Coder 480B A35B Instruct run on NVIDIA GB200 192GB?

Q: What should I upgrade first if Qwen3-Coder 480B A35B Instruct feels slow on NVIDIA GB200 192GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

A81Great

Estimated from fit model

Qwen3-Coder 480B A35B Instruct needs ~210.2 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q2_K quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

Qwen3-Coder 480B A35B Instruct at Q4_K_M needs 315.8 GB — too much for NVIDIA GB200 192GB (192.0 GB). Runs at Q2_K (210.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 315.8 GB, exceeds 192.0 GB available

315.8 GB required192.0 GB available

164% VRAM needed

123.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

26.3 tok/s

TTFT

7363 ms

Safe context

Memory

315.8 GB / 192.0 GB

Offload

40%

Memory breakdown

Weights292.8 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 480B A35B Instruct on NVIDIA GB200 192GB
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: 26.3 tok/s decode · 7.4s TTFT (warm) · 66 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 16.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	26.5 tok/s	3986 ms	4K
Coding	F	Too heavy	26.3 tok/s	7363 ms	4K
Agentic Coding	F	Too heavy	25.9 tok/s	10871 ms	4K
Reasoning	F	Too heavy	26.3 tok/s	8702 ms	4K
RAG	F	Too heavy	25.9 tok/s	13589 ms	4K

Quantization options

How Qwen3-Coder 480B A35B Instruct (480B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	187.2 GB	Low	F0
Q3_K_S	3	235.2 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Qwen3-Coder 480B A35B Instruct on your machine.

Run

lms load Qwen3-Coder-480B-A35B-Instruct && lms server start

Upgrade options

Hardware that runs Qwen3-Coder 480B A35B Instruct well

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.35.3 tok/s decode

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

Raises estimated decode speed by about 34%.

~$8,000 MSRP

Frequently asked questions

Can NVIDIA GB200 192GB run Qwen3-Coder 480B A35B Instruct?

Yes, NVIDIA GB200 192GB can run Qwen3-Coder 480B A35B Instruct at Q2_K quantization (Very compromised (needs ~16.2 GB host RAM)). The recommended Q4_K_M requires 315.8 GB which exceeds available memory, but at Q2_K it needs only 210.2 GB. Expected decode speed: 68.4 tok/s.

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

Qwen3-Coder 480B A35B Instruct (480B parameters) requires approximately 315.8 GB at Q4_K_M quantization. On NVIDIA GB200 192GB, it fits at Q2_K using 210.2 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA GB200 192GB the best fitting quantization is Q2_K, which uses 210.2 GB.

What speed will Qwen3-Coder 480B A35B Instruct run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Qwen3-Coder 480B A35B Instruct achieves approximately 68.4 tokens per second decode speed with a time-to-first-token of 2829ms using Q2_K quantization.

Can NVIDIA GB200 192GB run Qwen3-Coder 480B A35B Instruct for coding?

For coding workloads, Qwen3-Coder 480B A35B Instruct on NVIDIA GB200 192GB receives a F grade with 26.3 tok/s and 4K context.

What context window can Qwen3-Coder 480B A35B Instruct use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Qwen3-Coder 480B A35B Instruct can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA GB200 192GB See all hardware for Qwen3-Coder 480B A35B Instruct

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