RX 7900 XT 20GBBudget pick
800 GB/s (+527)
C
Raises estimated decode speed by about 148%.98.4 tok/s decode
Raises estimated decode speed by about 148%.
~$899 MSRP
Meta Llama 3 8B Instruct needs ~9.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 tok/s.
Operating mode
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
Fit status
Runs well
Decode
39.6 tok/s
TTFT
4885 ms
Safe context
152K
Memory
9.3 GB / 17.3 GB
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.
| 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 |
How Meta Llama 3 8B Instruct (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 |
Copy-paste commands to run Meta Llama 3 8B Instruct on your machine.
Run
lms load hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf && lms server startUpgrade options
Raises estimated decode speed by about 148%.
~$899 MSRP
RTX A4500 20GBBest valueRaises estimated decode speed by about 158%.
~$2,000 MSRP
Yes, MacBook Pro M4 Pro 24GB can run Meta Llama 3 8B Instruct with a C grade (Runs well). Expected decode speed: 39.6 tok/s.
Meta Llama 3 8B Instruct (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, Meta Llama 3 8B Instruct achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4885ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3 8B Instruct on MacBook Pro M4 Pro 24GB receives a C grade with 39.6 tok/s and 152K context.
On MacBook Pro M4 Pro 24GB, Meta Llama 3 8B Instruct can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.