Can Codestral 22B v0.1 run on NVIDIA A16 64GB?

YES — Runs Great

C47Usable

Estimated from fit model

Codestral 22B v0.1 needs ~23.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 23.6 GB, 34.9 tok/s, Runs well

23.6 GB required64.0 GB available

37% VRAM used

Fit status

Runs well

Decode

34.9 tok/s

TTFT

5552 ms

Safe context

267K

Memory

23.6 GB / 64.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on NVIDIA A16 64GB
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: 34.9 tok/s decode · 5.6s TTFT (warm) · 87 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	34.9 tok/s	3028 ms	267K
Coding	C	Runs well	34.9 tok/s	5552 ms	267K
Agentic Coding	C	Runs well	34.9 tok/s	8075 ms	267K
Reasoning	C	Runs well	34.9 tok/s	6561 ms	267K
RAG	C	Runs well	34.9 tok/s	10094 ms	267K

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 A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C41
Q3_K_S	3	10.8 GB	Low	C41
NVFP4	4	12.3 GB	Medium	C42
Q4_K_M	4	13.4 GB	Medium	C42
Q5_K_M	5	15.8 GB	High	C42
Q6_K	6	18.0 GB	High	C43
Q8_0	8	23.5 GB	Very High	C44
F16Best for your GPU	16	45.1 GB	Maximum	C48

Get started

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

Run

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

Opciones de mejora

Hardware que ejecuta bien Codestral 22B v0.1

RTX PRO 6000 Blackwell Workstation Edition 96GBOpción económica

96 GB VRAM (+32)1792 GB/s (+1192)

Sube la velocidad estimada de decodificación alrededor de un 221%.112.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 221%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBMejor relación calidad-precio

96 GB VRAM (+32)1597 GB/s (+997)

Sube la velocidad estimada de decodificación alrededor de un 187%.100 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 187%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$9,999 MSRP

NVIDIA H20 96GBMejora NVIDIA

96 GB VRAM (+32)4000 GB/s (+3400)

Sube la velocidad estimada de decodificación alrededor de un 592%.241.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 592%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Codestral 22B v0.1?

Yes, NVIDIA A16 64GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 34.9 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 23.6 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 A16 64GB?

On NVIDIA A16 64GB, Codestral 22B v0.1 achieves approximately 34.9 tokens per second decode speed with a time-to-first-token of 5552ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA A16 64GB receives a C grade with 34.9 tok/s and 267K context.

What context window can Codestral 22B v0.1 use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Codestral 22B v0.1 can safely use up to 267K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Codestral 22B v0.1

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