Can Codestral Mamba 7B run on NVIDIA T4 16GB?

YES — Runs Great

A75Great

○Estimated from fit model

Codestral Mamba 7B needs ~7.3 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 7.3 GB, 56.0 tok/s, Runs well

7.3 GB required16.0 GB available

46% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3456 ms

Safe context

262K

Memory

7.3 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on NVIDIA T4 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	56.0 tok/s	1885 ms	262K
Coding	A	Runs well	56.0 tok/s	3456 ms	262K
Agentic Coding	A	Runs well	56.0 tok/s	5027 ms	262K
Reasoning	A	Runs well	56.0 tok/s	4084 ms	262K
RAG	A	Runs well	56.0 tok/s	6284 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A72
Q3_K_S	3	3.4 GB	Low	A72
NVFP4	4	3.9 GB	Medium	A73
Q4_K_M	4	4.3 GB	Medium	A73
Q5_K_M	5	5.0 GB	High	A74
Q6_K	6	5.7 GB	High	A75
Q8_0Best for your GPU	8	7.5 GB	Very High	A76
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral Mamba 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
 --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your NVIDIA T4 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	40.7 tok/s
👁 Alibaba Qwen 3 14B	14B	S	26.3 tok/s
👁 Alibaba Qwen 3 8B	8B	S	45.8 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	24.9 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	23.2 tok/s

Frequently asked questions

See all results for NVIDIA T4 16GB See all hardware for Codestral Mamba 7B

URL: https://willitrunai.com/can-run/codestral-mamba-7b-on-t4-16gb

⇱ Codestral Mamba 7B on NVIDIA T4 16GB? YES