Can Codestral 22B v0.1 run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C46Usable

Estimated from fit model

Codestral 22B v0.1 needs ~31.3 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~300 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) — 31.3 GB, 300.4 tok/s, Runs well

31.3 GB required141.0 GB available

22% VRAM used

Fit status

Runs well

Decode

300.4 tok/s

TTFT

644 ms

Safe context

697K

Memory

31.3 GB / 141.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on NVIDIA H200 PCIe 141GB
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: 300.4 tok/s decode · 644ms TTFT (warm) · 751 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	300.4 tok/s	351 ms	697K
Coding	C	Runs well	300.4 tok/s	644 ms	697K
Agentic Coding	C	Runs well	300.4 tok/s	937 ms	697K
Reasoning	C	Runs well	300.4 tok/s	762 ms	697K
RAG	C	Runs well	300.4 tok/s	1172 ms	697K

Inference speed

Codestral 22B v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~90 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	89.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	57.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	51.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	48.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	41.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.3	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	8.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	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 22B v0.1 (22B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D38
Q3_K_S	3	10.8 GB	Low	D38
NVFP4	4	12.3 GB	Medium	D38
Q4_K_M	4	13.4 GB	Medium	D38
Q5_K_M	5	15.8 GB	High	D38
Q6_K	6	18.0 GB	High	D38
Q8_0	8	23.5 GB	Very High	D39
F16Best for your GPU	16	45.1 GB	Maximum	C42

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Codestral 22B v0.1?

Yes, NVIDIA H200 PCIe 141GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 300.4 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 31.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Codestral 22B v0.1 achieves approximately 300.4 tokens per second decode speed with a time-to-first-token of 644ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA H200 PCIe 141GB receives a C grade with 300.4 tok/s and 697K context.

What context window can Codestral 22B v0.1 use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Codestral 22B v0.1 can safely use up to 697K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for Codestral 22B v0.1

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