RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視1792 GB/s (+992)
C
Raises estimated decode speed by about 225%.129.9 tok/sデコード
Raises estimated decode speed by about 225%.
〜$9,999 MSRP
Can Codestral RAG 19B Pruned i1 run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
C45Usable
Estimated from fit model
Codestral RAG 19B Pruned i1 needs ~28.5 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 28.5 GB, 40.0 tok/s, Runs well
28.5 GB required92.2 GB available
31% VRAM used
Fit status
Runs well
Decode
40.0 tok/s
TTFT
4836 ms
Safe context
473K
Memory
28.5 GB / 92.2 GB
Memory breakdown
Weights11.6 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsCodestral RAG 19B Pruned i1 on Mac Studio M2 Ultra 128GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 40.0 tok/s | 2638 ms | 473K |
| Coding | C | Runs well | 40.0 tok/s | 4836 ms | 473K |
| Agentic Coding | C | Runs well | 40.0 tok/s | 7034 ms | 473K |
| Reasoning | C | Runs well | 40.0 tok/s | 5715 ms | 473K |
| RAG | C | Runs well | 40.0 tok/s | 8792 ms | 473K |
Quantization options
How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | D39 |
Q3_K_S | 3 | 9.3 GB | Low | D39 |
NVFP4 | 4 | 10.6 GB | Medium | D39 |
Q4_K_M | 4 | 11.6 GB | Medium | D39 |
Q5_K_M | 5 | 13.7 GB | High | D39 |
Q6_K | 6 | 15.6 GB | High | D40 |
Q8_0 | 8 | 20.3 GB | Very High | C40 |
F16Best for your GPU | 16 | 38.9 GB | Maximum | C44 |
Get started
Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.
Run
lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server startアップグレードオプション
Codestral RAG 19B Pruned i1を快適に動かすハードウェア
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run Codestral RAG 19B Pruned i1?
Yes, Mac Studio M2 Ultra 128GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 40.0 tok/s.
How much VRAM does Codestral RAG 19B Pruned i1 need?
Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 28.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral RAG 19B Pruned i1?
The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral RAG 19B Pruned i1 run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral RAG 19B Pruned i1 achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4836ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run Codestral RAG 19B Pruned i1 for coding?
For coding workloads, Codestral RAG 19B Pruned i1 on Mac Studio M2 Ultra 128GB receives a C grade with 40.0 tok/s and 473K context.
What context window can Codestral RAG 19B Pruned i1 use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral RAG 19B Pruned i1 can safely use up to 473K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Codestral RAG 19B Pruned i1?
Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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