Can Qwen3-Coder 30B A3B Instruct run on NVIDIA H20 96GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~30.9 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~490 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 30.9 GB, 489.9 tok/s, Runs well

30.9 GB required96.0 GB available

32% VRAM used

Fit status

Runs well

Decode

489.9 tok/s

TTFT

395 ms

Safe context

256K

Memory

30.9 GB / 96.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on NVIDIA H20 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: 489.9 tok/s decode · 395ms TTFT (warm) · 1225 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	489.9 tok/s	350 ms	256K
Coding	S	Runs well	489.9 tok/s	395 ms	256K
Agentic Coding	S	Runs well	489.9 tok/s	575 ms	256K
Reasoning	S	Runs well	489.9 tok/s	467 ms	256K
RAG	S	Runs well	489.9 tok/s	718 ms	256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	A83
Q3_K_S	3	14.9 GB	Low	A83
NVFP4	4	17.1 GB	Medium	A83
Q4_K_M	4	18.6 GB	Medium	A84
Q5_K_M	5	22.0 GB	High	A84
Q6_K	6	25.0 GB	High	A84
Q8_0	8	32.6 GB	Very High	S86
F16Best for your GPU	16	62.5 GB	Maximum	S91

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s

Frequently asked questions

Can NVIDIA H20 96GB run Qwen3-Coder 30B A3B Instruct?

Yes, NVIDIA H20 96GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 489.9 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 30.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen3-Coder 30B A3B Instruct achieves approximately 489.9 tokens per second decode speed with a time-to-first-token of 395ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on NVIDIA H20 96GB receives a S grade with 489.9 tok/s and 256K context.

What context window can Qwen3-Coder 30B A3B Instruct use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen3-Coder 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Qwen3-Coder 30B A3B Instruct

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<iframe src="https://willitrunai.com/embed/qwen-3-coder-30b-a3b-on-h20-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

489.9 tok/s

350 ms

256K

Coding

Runs well

489.9 tok/s

395 ms

256K

Agentic Coding

Runs well

489.9 tok/s

575 ms

256K

Reasoning

Runs well

489.9 tok/s

467 ms

256K

RAG

Runs well

489.9 tok/s

718 ms

256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	A83
Q3_K_S	3	14.9 GB	Low	A83
NVFP4	4	17.1 GB	Medium	A83
Q4_K_M	4	18.6 GB	Medium	A84
Q5_K_M	5	22.0 GB	High	A84
Q6_K	6	25.0 GB	High	A84
Q8_0	8	32.6 GB	Very High	S86
F16Best for your GPU	16	62.5 GB	Maximum	S91

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

47 tok/s