RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+104)
C
Raises estimated decode speed by about 57%.64 tok/sデコード
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can Mamba Codestral 7B v0.1 run on GTX 1070 Ti 8GB?
YES — Tight Fit
C51Usable
Estimated from fit model
Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 6.8 GB, 40.7 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
40.7 tok/s
TTFT
4759 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsMamba Codestral 7B v0.1 on GTX 1070 Ti 8GB
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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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 | C | Runs well | 40.7 tok/s | 2596 ms | 40K |
| Coding | C | Tight fit | 40.7 tok/s | 4759 ms | 40K |
| Agentic Coding | C | Runs with offload | 40.7 tok/s | 6923 ms | 40K |
| Reasoning | C | Tight fit | 40.7 tok/s | 5625 ms | 40K |
| RAG | C | Runs with offload | 40.7 tok/s | 8653 ms | 40K |
Inference speed
Mamba Codestral 7B v0.1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Mamba Codestral 7B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 92.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 92.6 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 64.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 59.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 53.5 | Tight |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
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 startアップグレードオプション
Mamba Codestral 7B v0.1を快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+8)448 GB/s (+192)
C
Raises estimated decode speed by about 84%.74.8 tok/sデコード
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RTX 5070 12GBNVIDIAアップグレード12 GB VRAM (+4)672 GB/s (+416)
C
Raises estimated decode speed by about 180%.114 tok/sデコード
Raises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
Frequently asked questions
Can GTX 1070 Ti 8GB run Mamba Codestral 7B v0.1?
Yes, GTX 1070 Ti 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 40.7 tok/s.
How much VRAM does Mamba Codestral 7B v0.1 need?
Mamba Codestral 7B v0.1 (7B parameters) requires approximately 6.8 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 GTX 1070 Ti 8GB?
On GTX 1070 Ti 8GB, Mamba Codestral 7B v0.1 achieves approximately 40.7 tokens per second decode speed with a time-to-first-token of 4759ms using Q4_K_M quantization.
Can GTX 1070 Ti 8GB run Mamba Codestral 7B v0.1 for coding?
For coding workloads, Mamba Codestral 7B v0.1 on GTX 1070 Ti 8GB receives a C grade with 40.7 tok/s and 40K context.
What context window can Mamba Codestral 7B v0.1 use on GTX 1070 Ti 8GB?
On GTX 1070 Ti 8GB, Mamba Codestral 7B v0.1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: