20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.75.6 tok/s decode
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Mamba Codestral 7B v0.1 run on NVIDIA A2 16GB?
YES — Runs Great
C49Usable
○Estimated from fit model
Mamba Codestral 7B v0.1 needs ~7.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.6 GB, 42.0 tok/s, Runs well
7.6 GB required16.0 GB available
48% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4608 ms
Safe context
180K
Memory
7.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsMamba Codestral 7B v0.1 on NVIDIA A2 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | 42.0 tok/s | 2513 ms | 180K |
| Coding | C | Runs well | 42.0 tok/s | 4608 ms | 180K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6703 ms | 180K |
| Reasoning | C | Runs well | 42.0 tok/s | 5446 ms | 180K |
| RAG | C | Runs well | 42.0 tok/s | 8378 ms | 180K |
Quantization options
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 | 3.9 GB | Medium | C47 |
Q4_K_M | 4 | 4.3 GB | Medium | C48 |
Q5_K_M | 5 | 5.0 GB | High | C48 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.
Run
lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server startUpgrade options