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URL: https://willitrunai.com/can-run/mistral-small-4-119b-on-rtx-5070-ti-16gb

⇱ Mistral Small 4 119B on RTX 5070 Ti 16GB? No — Alternatives

Can Mistral Small 4 119B run on RTX 5070 Ti 16GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Mistral Small 4 119B needs ~80.5 GB but RTX 5070 Ti 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity
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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) — 80.5 GB, exceeds 16.0 GB available
80.5 GB required16.0 GB available
503% VRAM needed

64.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.7 tok/s

TTFT

51636 ms

Safe context

4K

Memory

80.5 GB / 16.0 GB

Offload

80%

Memory breakdown

Weights72.6 GB
KV Cache5.4 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMistral Small 4 119B on RTX 5070 Ti 16GB
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: 3.7 tok/s decode · 51.6s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 80.5 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy3.7 tok/s28165 ms4K
CodingFToo heavy3.7 tok/s51636 ms4K
Agentic CodingFToo heavy3.7 tok/s75106 ms4K
ReasoningFToo heavy3.7 tok/s61024 ms4K
RAGFToo heavy3.7 tok/s93883 ms4K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
46.4 GB
LowF0
Q3_K_S
3
58.3 GB
LowF0
NVFP4
4
66.6 GB
MediumF0
Q4_K_M
4
72.6 GB
MediumF0
Q5_K_M
5
85.7 GB
HighF0
Q6_K
6
97.6 GB
HighF0
Q8_0
8
127.3 GB
Very HighF0
F16
16
244.0 GB
MaximumF0

Upgrade options

Hardware that runs Mistral Small 4 119B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick
96 GB VRAM (+80)1792 GB/s (+896)
S
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

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value
96 GB VRAM (+80)1597 GB/s (+701)
S
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
NVIDIA DGX Spark 128GBNVIDIA upgrade
128 GB Unified (+112)
S
Makes the model fit on the accelerator instead of staying completely out of reach.7.1 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.

Frequently asked questions

See all results for RTX 5070 Ti 16GBSee all hardware for Mistral Small 4 119B