Intel Arc B580 12GBコスパ最良456 GB/s (+336)
C
Raises estimated decode speed by about 175%.44.9 tok/sデコード
Raises estimated decode speed by about 175%.
〜$249 MSRP
Can Meta Llama 3.1 8B Instruct run on MacBook Pro M4 16GB?
YES — Runs Great
C52Usable
Estimated — low-sample bucket· few comparable runs
Meta Llama 3.1 8B Instruct needs ~8.4 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~16 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) — 8.4 GB, 16.3 tok/s, Runs well
8.4 GB required11.5 GB available
73% VRAM used
Fit status
Runs well
Decode
16.3 tok/s
TTFT
11886 ms
Safe context
68K
Memory
8.4 GB / 11.5 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsMeta Llama 3.1 8B Instruct on MacBook Pro M4 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 16.3 tok/s decode · 11.9s TTFT (warm) · 41 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 | 16.3 tok/s | 6483 ms | 68K |
| Coding | C | Runs well | 16.3 tok/s | 11886 ms | 68K |
| Agentic Coding | C | Runs well | 16.3 tok/s | 17288 ms | 68K |
| Reasoning | C | Runs well | 16.3 tok/s | 14047 ms | 68K |
| RAG | C | Runs well | 16.3 tok/s | 21610 ms | 68K |
Quantization options
How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C50 |
Q3_K_S | 3 | 3.9 GB | Low | C51 |
NVFP4 | 4 | 4.5 GB | Medium | C52 |
Q4_K_M | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | C53 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | C52 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Meta Llama 3.1 8B Instruct on your machine.
Run
lms load hf-bartowski--meta-llama-3-1-8b-instruct-gguf && lms server startアップグレードオプション
Meta Llama 3.1 8B Instructを快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M4 16GB run Meta Llama 3.1 8B Instruct?
Yes, MacBook Pro M4 16GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 16.3 tok/s.
How much VRAM does Meta Llama 3.1 8B Instruct need?
Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 8.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Meta Llama 3.1 8B Instruct?
The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Meta Llama 3.1 8B Instruct run at on MacBook Pro M4 16GB?
On MacBook Pro M4 16GB, Meta Llama 3.1 8B Instruct achieves approximately 16.3 tokens per second decode speed with a time-to-first-token of 11886ms using Q4_K_M quantization.
Can MacBook Pro M4 16GB run Meta Llama 3.1 8B Instruct for coding?
For coding workloads, Meta Llama 3.1 8B Instruct on MacBook Pro M4 16GB receives a C grade with 16.3 tok/s and 68K context.
What context window can Meta Llama 3.1 8B Instruct use on MacBook Pro M4 16GB?
On MacBook Pro M4 16GB, Meta Llama 3.1 8B Instruct can safely use up to 68K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Meta Llama 3.1 8B Instruct?
Not always. MacBook Pro M4 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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