MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.8 tok/s 解码
~$2,499 MSRP
Can speechless zephyr code functionary 7b run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
speechless zephyr code functionary 7b needs ~9.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.2 GB, 88.4 tok/s, Runs well
9.2 GB required32.0 GB available
29% VRAM used
Fit status
Runs well
Decode
88.4 tok/s
TTFT
2189 ms
Safe context
461K
Memory
9.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsspeechless zephyr code functionary 7b on Radeon AI PRO R9700 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: 88.4 tok/s decode · 2.2s TTFT (warm) · 221 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 | 88.4 tok/s | 1194 ms | 461K |
| Coding | C | Runs well | 88.4 tok/s | 2189 ms | 461K |
| Agentic Coding | C | Runs well | 88.4 tok/s | 3184 ms | 461K |
| Reasoning | C | Runs well | 88.4 tok/s | 2587 ms | 461K |
| RAG | C | Runs well | 88.4 tok/s | 3981 ms | 461K |
Quantization options
How speechless zephyr code functionary 7b (7B params) fits at each quantization level on Radeon AI PRO R9700 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升级选项
能流畅运行 speechless zephyr code functionary 7b 的硬件
Frequently asked questions
Can Radeon AI PRO R9700 32GB run speechless zephyr code functionary 7b?
Yes, Radeon AI PRO R9700 32GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 88.4 tok/s.
How much VRAM does speechless zephyr code functionary 7b need?
speechless zephyr code functionary 7b (7B parameters) requires approximately 9.2 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 Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, speechless zephyr code functionary 7b achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run speechless zephyr code functionary 7b for coding?
For coding workloads, speechless zephyr code functionary 7b on Radeon AI PRO R9700 32GB receives a C grade with 88.4 tok/s and 461K context.
What context window can speechless zephyr code functionary 7b use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, speechless zephyr code functionary 7b can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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