Intel Arc B580 12GBコスパ最良456 GB/s (+356)
C
Raises estimated decode speed by about 237%.51.3 tok/sデコード
Raises estimated decode speed by about 237%.
〜$249 MSRP
Can speechless zephyr code functionary 7b run on MacBook Air M2 16GB?
YES — Runs Great
C50Usable
Estimated from fit model
speechless zephyr code functionary 7b needs ~7.7 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~15 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 7.7 GB, 15.2 tok/s, Runs well
7.7 GB required11.5 GB available
67% VRAM used
Fit status
Runs well
Decode
15.2 tok/s
TTFT
12718 ms
Safe context
90K
Memory
7.7 GB / 11.5 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsspeechless zephyr code functionary 7b on MacBook Air M2 16GB
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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 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 | 15.2 tok/s | 6937 ms | 90K |
| Coding | C | Runs well | 15.2 tok/s | 12718 ms | 90K |
| Agentic Coding | C | Runs well | 15.2 tok/s | 18499 ms | 90K |
| Reasoning | C | Runs well | 15.2 tok/s | 15030 ms | 90K |
| RAG | C | Runs well | 15.2 tok/s | 23124 ms | 90K |
Quantization options
How speechless zephyr code functionary 7b (7B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 | 3.9 GB | Medium | C51 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 MacBook Air M2 16GB run speechless zephyr code functionary 7b?
Yes, MacBook Air M2 16GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 15.2 tok/s.
How much VRAM does speechless zephyr code functionary 7b need?
speechless zephyr code functionary 7b (7B parameters) requires approximately 7.7 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 MacBook Air M2 16GB?
On MacBook Air M2 16GB, speechless zephyr code functionary 7b achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.
Can MacBook Air M2 16GB run speechless zephyr code functionary 7b for coding?
For coding workloads, speechless zephyr code functionary 7b on MacBook Air M2 16GB receives a C grade with 15.2 tok/s and 90K context.
What context window can speechless zephyr code functionary 7b use on MacBook Air M2 16GB?
On MacBook Air M2 16GB, speechless zephyr code functionary 7b can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Air M2 16GB as fast as VRAM for speechless zephyr code functionary 7b?
Not always. MacBook Air M2 16GB 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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