Meta Llama 3.1 8B Instruct needs ~9.4 GB VRAM. NVIDIA L4 24GB has 24.0 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
40.0 tok/s
TTFT
4845 ms
Safe context
265K
Memory
9.4 GB / 24.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 40.0 tok/s | 2643 ms | 265K |
| Coding | C | Runs well | 40.0 tok/s | 4845 ms | 265K |
| Agentic Coding | C | Runs well | 40.0 tok/s | 7047 ms | 265K |
| Reasoning | C | Runs well | 40.0 tok/s | 5726 ms | 265K |
| RAG | C | Runs well | 40.0 tok/s | 8809 ms | 265K |
Inference speed
Estimated decode speed (tokens/sec) for Meta Llama 3.1 8B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C46 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C47 |
Q8_0 | 8 | 8.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
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 startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRaises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, NVIDIA L4 24GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 40.0 tok/s.
Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA L4 24GB, Meta Llama 3.1 8B Instruct achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3.1 8B Instruct on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.
On NVIDIA L4 24GB, Meta Llama 3.1 8B Instruct can safely use up to 265K 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-bartowski--meta-llama-3-1-8b-instruct-gguf-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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