Can Mistral Small 4 119B run on NVIDIA A16 64GB?

Q: What should I upgrade first if Mistral Small 4 119B feels slow on NVIDIA A16 64GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

A78Great

Estimated from fit model

Mistral Small 4 119B needs ~71.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q3_K_S quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Mistral Small 4 119B at Q4_K_M needs 85.3 GB — too much for NVIDIA A16 64GB (64.0 GB). Runs at Q3_K_S (71.0 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 85.3 GB, exceeds 64.0 GB available

85.3 GB required64.0 GB available

133% VRAM needed

21.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.4 tok/s

TTFT

23144 ms

Safe context

Memory

85.3 GB / 64.0 GB

Offload

20%

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 4 119B 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: 8.4 tok/s decode · 23.1s TTFT (warm) · 21 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 5.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	8.9 tok/s	11802 ms	4K
Coding	F	Too heavy	8.4 tok/s	23144 ms	4K
Agentic Coding	F	Too heavy	7.4 tok/s	38284 ms	4K
Reasoning	F	Too heavy	8.4 tok/s	27352 ms	4K
RAG	F	Too heavy	6.8 tok/s	52043 ms	4K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	46.4 GB	Low	S88
Q3_K_S	3	58.3 GB	Low	F0

Get started

Copy-paste commands to run Mistral Small 4 119B on your machine.

Run

lms load Mistral-Small-4-119B-2603 && lms server start

Upgrade options

Hardware that runs Mistral Small 4 119B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.65.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.58.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.141.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Mistral Small 4 119B?

Yes, NVIDIA A16 64GB can run Mistral Small 4 119B at Q3_K_S quantization (Very compromised (needs ~5.7 GB host RAM)). The recommended Q4_K_M requires 85.3 GB which exceeds available memory, but at Q3_K_S it needs only 71.0 GB. Expected decode speed: 14.2 tok/s.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 85.3 GB at Q4_K_M quantization. On NVIDIA A16 64GB, it fits at Q3_K_S using 71.0 GB.

What is the best quantization for Mistral Small 4 119B?

The recommended quantization is Q4_K_M, but on NVIDIA A16 64GB the best fitting quantization is Q3_K_S, which uses 71.0 GB.

What speed will Mistral Small 4 119B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Mistral Small 4 119B achieves approximately 14.2 tokens per second decode speed with a time-to-first-token of 13591ms using Q3_K_S quantization.

Can NVIDIA A16 64GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on NVIDIA A16 64GB receives a F grade with 8.4 tok/s and 4K context.

What context window can Mistral Small 4 119B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Mistral Small 4 119B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Mistral Small 4 119B

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