Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+115)
B
Adds memory headroom for longer context windows and future model growth.98 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can CodeLlama 7B Instruct run on NVIDIA H200 PCIe 141GB?
YES — Runs Great
B69Good
Estimated from fit model
CodeLlama 7B Instruct needs ~27.4 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 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) — 27.4 GB, 98.0 tok/s, Runs well
27.4 GB required141.0 GB available
19% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
16K
Memory
27.4 GB / 141.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom14.1 GB
See how fast it feels
See how fast it feelsCodeLlama 7B Instruct on NVIDIA H200 PCIe 141GB
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 | B | Runs well | 98.0 tok/s | 1078 ms | 16K |
| Coding | B | Runs well | 98.0 tok/s | 1976 ms | 16K |
| Agentic Coding | A | Runs well | 98.0 tok/s | 2873 ms | 16K |
| Reasoning | B | Runs well | 98.0 tok/s | 2335 ms | 16K |
| RAG | A | Runs well | 98.0 tok/s | 3592 ms | 16K |
Inference speed
CodeLlama 7B Instruct inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for CodeLlama 7B Instruct 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 | Tight |
| 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 | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 43.0 | Heavy offload |
| RTX 3060 12GB | 12 GB | Q4_K_M | 24.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 10.6 | Too big |
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 CodeLlama 7B Instruct (7B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B61 |
NVFP4 | 4 | 3.9 GB | Medium | B61 |
Q4_K_M | 4 | 4.3 GB | Medium | B61 |
Q5_K_M | 5 | 5.0 GB | High | B61 |
Q6_K | 6 | 5.7 GB | High | B61 |
Q8_0 | 8 | 7.5 GB | Very High | B61 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B62 |
Get started
Copy-paste commands to run CodeLlama 7B Instruct on your machine.
Run
lms load CodeLlama-7b-Instruct-hf && lms server startアップグレードオプション
CodeLlama 7B Instructを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA H200 PCIe 141GB run CodeLlama 7B Instruct?
Yes, NVIDIA H200 PCIe 141GB can run CodeLlama 7B Instruct with a B grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does CodeLlama 7B Instruct need?
CodeLlama 7B Instruct (7B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.
What is the best quantization for CodeLlama 7B Instruct?
The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will CodeLlama 7B Instruct run at on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, CodeLlama 7B Instruct achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can NVIDIA H200 PCIe 141GB run CodeLlama 7B Instruct for coding?
For coding workloads, CodeLlama 7B Instruct on NVIDIA H200 PCIe 141GB receives a B grade with 98.0 tok/s and 16K context.
What context window can CodeLlama 7B Instruct use on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, CodeLlama 7B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
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