Can Qwen3-Coder-Next run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

S97Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~59.5 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~114 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) — 59.5 GB, 113.5 tok/s, Runs well

59.5 GB required80.0 GB available

74% VRAM used

Fit status

Runs well

Decode

113.5 tok/s

TTFT

1706 ms

Safe context

240K

Memory

59.5 GB / 80.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on NVIDIA H100 PCIe 80GB
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: 113.5 tok/s decode · 1.7s TTFT (warm) · 284 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	113.5 tok/s	931 ms	240K
Coding	S	Runs well	113.5 tok/s	1706 ms	240K
Agentic Coding	S	Runs well	113.5 tok/s	2482 ms	240K
Reasoning	S	Runs well	113.5 tok/s	2017 ms	240K
RAG	S	Runs well	113.5 tok/s	3103 ms	240K

Inference speed

Qwen3-Coder-Next inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder-Next at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~49 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	48.9	Fits
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	43.1	Heavy offload
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	40.7	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	39.3	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	38.6	Fits
2× RTX 3090 24GB	48 GB	Q4_K_M	33.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.2	Fits
4× RTX 3060 12GB	48 GB	Q4_K_M	29.6	Heavy offload
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.7	Too big
RTX 5090 32GB	32 GB	Q4_K_M	20.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	10.8	Too big
RTX 4090 24GB	24 GB	Q4_K_M	7.8	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	6.6	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	6.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A85
Q3_K_S	3	39.2 GB	Low	S87
NVFP4	4	44.8 GB	Medium	S88
Q4_K_M	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen 3.5 122B A10B	122B	A	44.5 tok/s
Mistral Small 4 119B	119B	A	47 tok/s
GPT-OSS 120B	117B	A	17.1 tok/s
Command A 111B	111B	S	20.3 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen3-Coder-Next?

Yes, NVIDIA H100 PCIe 80GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 113.5 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 59.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen3-Coder-Next achieves approximately 113.5 tokens per second decode speed with a time-to-first-token of 1706ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA H100 PCIe 80GB receives a S grade with 113.5 tok/s and 240K context.

What context window can Qwen3-Coder-Next use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen3-Coder-Next can safely use up to 240K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Qwen3-Coder-Next

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-h100-pcie-80gb" 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

113.5 tok/s

931 ms

240K

Coding

Runs well

113.5 tok/s

1706 ms

240K

Agentic Coding

Runs well

113.5 tok/s

2482 ms

240K

Reasoning

Runs well

113.5 tok/s

2017 ms

240K

RAG

Runs well

113.5 tok/s

3103 ms

240K

Qwen3-Coder-Next inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

48.9

Fits

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

43.1

Heavy offload

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

40.7

Fits

2× RTX 4090 24GB

48 GB

Q4_K_M

39.3

Heavy offload

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

38.6

Fits

2× RTX 3090 24GB

48 GB

Q4_K_M

33.6

Heavy offload

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

30.2

Fits

4× RTX 3060 12GB

48 GB

Q4_K_M

29.6

Heavy offload

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

21.7

Too big

RTX 5090 32GB

32 GB

Q4_K_M

20.8

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

16.6

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

15.3

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

10.8

Too big

RTX 4090 24GB

24 GB

Q4_K_M

7.8

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

7.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

6.6

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

6.2

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.8

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.4

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A85
Q3_K_S	3	39.2 GB	Low	S87
NVFP4	4	44.8 GB	Medium	S88
Q4_K_M	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

14.8 tok/s

Qwen 3.5 122B A10B

122B

44.5 tok/s

Mistral Small 4 119B

119B

47 tok/s

GPT-OSS 120B

117B

17.1 tok/s

Command A 111B

111B

20.3 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen3-Coder-Next?

Yes, NVIDIA H100 PCIe 80GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 113.5 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 59.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen3-Coder-Next achieves approximately 113.5 tokens per second decode speed with a time-to-first-token of 1706ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA H100 PCIe 80GB receives a S grade with 113.5 tok/s and 240K context.

What context window can Qwen3-Coder-Next use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen3-Coder-Next can safely use up to 240K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.