Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+115)
B
Adds memory headroom for longer context windows and future model growth.98 tok/s decode
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
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) — 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 |
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 |
Get started
Copy-paste commands to run CodeLlama 7B Instruct on your machine.
Run
lms load CodeLlama-7b-Instruct-hf && lms server startUpgrade options