MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.8 tok/sデコード
〜$2,499 MSRP
Can speechless zephyr code functionary 7b run on NVIDIA V100 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
speechless zephyr code functionary 7b needs ~9.5 GB VRAM. NVIDIA V100 32GB has 32.0 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) — 9.5 GB, 98.0 tok/s, Runs well
9.5 GB required32.0 GB available
30% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
455K
Memory
9.5 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsspeechless zephyr code functionary 7b on NVIDIA V100 32GB
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 | 455K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 455K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 455K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 455K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 455K |
Quantization options
How speechless zephyr code functionary 7b (7B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Get started
Copy-paste commands to run speechless zephyr code functionary 7b on your machine.
Run
lms load hf-uukuguy--speechless-zephyr-code-functionary-7b && lms server startアップグレードオプション
speechless zephyr code functionary 7bを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA V100 32GB run speechless zephyr code functionary 7b?
Yes, NVIDIA V100 32GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does speechless zephyr code functionary 7b need?
speechless zephyr code functionary 7b (7B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
What is the best quantization for speechless zephyr code functionary 7b?
The recommended quantization for speechless zephyr code functionary 7b is Q4_K_M, which balances quality and memory efficiency.
What speed will speechless zephyr code functionary 7b run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, speechless zephyr code functionary 7b achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run speechless zephyr code functionary 7b for coding?
For coding workloads, speechless zephyr code functionary 7b on NVIDIA V100 32GB receives a C grade with 98.0 tok/s and 455K context.
What context window can speechless zephyr code functionary 7b use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, speechless zephyr code functionary 7b can safely use up to 455K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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