Mac Studio M3 Ultra 96GBGünstige Wahl
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.98 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Can Mamba Codestral 7B v0.1 run on NVIDIA L40 48GB?
YES — Runs Great
C46Usable
Estimated from fit model
Mamba Codestral 7B v0.1 needs ~11.1 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 11.1 GB, 98.0 tok/s, Runs well
11.1 GB required48.0 GB available
23% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
736K
Memory
11.1 GB / 48.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsMamba Codestral 7B v0.1 on NVIDIA L40 48GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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 | 98.0 tok/s | 1078 ms | 736K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 736K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 736K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 736K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 736K |
Quantization options
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C41 |
NVFP4 | 4 | 3.9 GB | Medium | C41 |
Q4_K_M | 4 | 4.3 GB | Medium | C41 |
Q5_K_M | 5 | 5.0 GB | High | C41 |
Q6_K | 6 | 5.7 GB | High | C41 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
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-Optionen
Hardware, die Mamba Codestral 7B v0.1 gut ausführt
Frequently asked questions
Can NVIDIA L40 48GB run Mamba Codestral 7B v0.1?
Yes, NVIDIA L40 48GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does Mamba Codestral 7B v0.1 need?
Mamba Codestral 7B v0.1 (7B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Mamba Codestral 7B v0.1?
The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mamba Codestral 7B v0.1 run at on NVIDIA L40 48GB?
On NVIDIA L40 48GB, Mamba Codestral 7B v0.1 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can NVIDIA L40 48GB run Mamba Codestral 7B v0.1 for coding?
For coding workloads, Mamba Codestral 7B v0.1 on NVIDIA L40 48GB receives a C grade with 98.0 tok/s and 736K context.
What context window can Mamba Codestral 7B v0.1 use on NVIDIA L40 48GB?
On NVIDIA L40 48GB, Mamba Codestral 7B v0.1 can safely use up to 736K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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