RX 7900 XT 20GBOpção econômica
800 GB/s (+527)
C
Raises estimated decode speed by about 148%.98.4 tok/s decodificação
Raises estimated decode speed by about 148%.
~$899 MSRP
Can llava llama 3 8b v1 1 run on MacBook Pro M4 Pro 24GB?
YES — Runs Great
C51Usable
Estimated — low-sample bucket· few comparable runs
llava llama 3 8b v1 1 needs ~9.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 9.3 GB, 39.6 tok/s, Runs well
9.3 GB required17.3 GB available
54% VRAM used
Fit status
Runs well
Decode
39.6 tok/s
TTFT
4885 ms
Safe context
152K
Memory
9.3 GB / 17.3 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsllava llama 3 8b v1 1 on MacBook Pro M4 Pro 24GB
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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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 | 39.6 tok/s | 2665 ms | 152K |
| Coding | C | Runs well | 39.6 tok/s | 4885 ms | 152K |
| Agentic Coding | C | Runs well | 39.6 tok/s | 7106 ms | 152K |
| Reasoning | C | Runs well | 39.6 tok/s | 5773 ms | 152K |
| RAG | C | Runs well | 39.6 tok/s | 8882 ms | 152K |
Quantization options
How llava llama 3 8b v1 1 (8B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C47 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C48 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C52 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run llava llama 3 8b v1 1 on your machine.
Run
lms load hf-xtuner--llava-llama-3-8b-v1-1-gguf && lms server startOpções de upgrade
Hardware que roda bem llava llama 3 8b v1 1
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run llava llama 3 8b v1 1?
Yes, MacBook Pro M4 Pro 24GB can run llava llama 3 8b v1 1 with a C grade (Runs well). Expected decode speed: 39.6 tok/s.
How much VRAM does llava llama 3 8b v1 1 need?
llava llama 3 8b v1 1 (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
What is the best quantization for llava llama 3 8b v1 1?
The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.
What speed will llava llama 3 8b v1 1 run at on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, llava llama 3 8b v1 1 achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4885ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run llava llama 3 8b v1 1 for coding?
For coding workloads, llava llama 3 8b v1 1 on MacBook Pro M4 Pro 24GB receives a C grade with 39.6 tok/s and 152K context.
What context window can llava llama 3 8b v1 1 use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, llava llama 3 8b v1 1 can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for llava llama 3 8b v1 1?
Not always. MacBook Pro M4 Pro 24GB 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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