Can Codestral 21B Pruned i1 run on NVIDIA H100 80GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~24.5 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~220 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) — 24.5 GB, 219.7 tok/s, Runs well

24.5 GB required80.0 GB available

31% VRAM used

Fit status

Runs well

Decode

219.7 tok/s

TTFT

881 ms

Safe context

377K

Memory

24.5 GB / 80.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on NVIDIA H100 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: 219.7 tok/s decode · 881ms TTFT (warm) · 549 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	C	Runs well	219.7 tok/s	481 ms	377K
Coding	C	Runs well	219.7 tok/s	881 ms	377K
Agentic Coding	C	Runs well	219.7 tok/s	1282 ms	377K
Reasoning	C	Runs well	219.7 tok/s	1042 ms	377K
RAG	C	Runs well	219.7 tok/s	1602 ms	377K

Inference speed

Codestral 21B Pruned i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 21B Pruned i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~94 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?
RTX 5090 32GB	32 GB	Q4_K_M	93.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	59.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	54.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	51.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	43.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	36.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	34.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.7	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	18.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	17.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.3	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 Codestral 21B Pruned i1 (21B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	D40
Q3_K_S	3	10.3 GB	Low	D40
NVFP4	4	11.8 GB	Medium	D40
Q4_K_M	4	12.8 GB	Medium	C40
Q5_K_M	5	15.1 GB	High	C40
Q6_K	6	17.2 GB	High	C41
Q8_0	8	22.5 GB	Very High	C42
F16Best for your GPU	16	43.1 GB	Maximum	C47

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Frequently asked questions

Can NVIDIA H100 80GB run Codestral 21B Pruned i1?

Yes, NVIDIA H100 80GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 219.7 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 21B Pruned i1 achieves approximately 219.7 tokens per second decode speed with a time-to-first-token of 881ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on NVIDIA H100 80GB receives a C grade with 219.7 tok/s and 377K context.

What context window can Codestral 21B Pruned i1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 21B Pruned i1 can safely use up to 377K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Codestral 21B Pruned i1

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codestral-21b-pruned-i1-gguf-on-h100-80gb" 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

219.7 tok/s

481 ms

377K

Coding

Runs well

219.7 tok/s

881 ms

377K

Agentic Coding

Runs well

219.7 tok/s

1282 ms

377K

Reasoning

Runs well

219.7 tok/s

1042 ms

377K

RAG

Runs well

219.7 tok/s

1602 ms

377K

Codestral 21B Pruned i1 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

93.7

Fits

RTX 4090 24GB

24 GB

Q4_K_M

59.8

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

54.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

51.1

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

43.5

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

36.2

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

35.2

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

35.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

34.3

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

27.7

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

22.2

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

18.7

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

17.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

9.8

Too big

RTX 3060 12GB

12 GB

Q4_K_M

6.2

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.3

Too big

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	D40
Q3_K_S	3	10.3 GB	Low	D40
NVFP4	4	11.8 GB	Medium	D40
Q4_K_M	4	12.8 GB	Medium	C40
Q5_K_M	5	15.1 GB	High	C40
Q6_K	6	17.2 GB	High	C41
Q8_0	8	22.5 GB	Very High	C42
F16Best for your GPU	16	43.1 GB	Maximum	C47

Frequently asked questions

Can NVIDIA H100 80GB run Codestral 21B Pruned i1?

Yes, NVIDIA H100 80GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 219.7 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 21B Pruned i1 achieves approximately 219.7 tokens per second decode speed with a time-to-first-token of 881ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on NVIDIA H100 80GB receives a C grade with 219.7 tok/s and 377K context.

What context window can Codestral 21B Pruned i1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 21B Pruned i1 can safely use up to 377K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.