Mac Studio M2 Ultra 64GBOpção econômica
64 GB Unified (+24)
C
This setup is broadly balanced for this model.98 tok/s decodificação
~$3,999 MSRP
Can speechless zephyr code functionary 7b run on NVIDIA A100 40GB?
YES — Runs Great
C47Usable
Estimated from fit model
speechless zephyr code functionary 7b needs ~10.3 GB VRAM. NVIDIA A100 40GB has 40.0 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) — 10.3 GB, 98.0 tok/s, Runs well
10.3 GB required40.0 GB available
26% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
595K
Memory
10.3 GB / 40.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom4.0 GB
See how fast it feels
See how fast it feelsspeechless zephyr code functionary 7b on NVIDIA A100 40GB
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 | 595K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 595K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 595K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 595K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 595K |
Quantization options
How speechless zephyr code functionary 7b (7B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C42 |
Q3_K_S | 3 | 3.4 GB | Low | C42 |
NVFP4 | 4 | 3.9 GB | Medium | C42 |
Q4_K_M | 4 | 4.3 GB | Medium | C42 |
Q5_K_M | 5 | 5.0 GB | High | C42 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C43 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C45 |
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 startOpções de upgrade
Hardware que roda bem speechless zephyr code functionary 7b
Frequently asked questions
Can NVIDIA A100 40GB run speechless zephyr code functionary 7b?
Yes, NVIDIA A100 40GB 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 10.3 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 A100 40GB?
On NVIDIA A100 40GB, 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 A100 40GB run speechless zephyr code functionary 7b for coding?
For coding workloads, speechless zephyr code functionary 7b on NVIDIA A100 40GB receives a C grade with 98.0 tok/s and 595K context.
What context window can speechless zephyr code functionary 7b use on NVIDIA A100 40GB?
On NVIDIA A100 40GB, speechless zephyr code functionary 7b can safely use up to 595K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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