Llama 3 · Llama-3.1 · HF: meta-llama/Llama-3.1-8B-Instruct
Llama 3.1 8B Instruct — VRAM, KV Cache & Hardware Matrix
Serverless API Rate
$0.02 in / $0.04 out per 1M
Parameters
8.03B Dense
Attention
GQA (8 KV / 32 Q)
Layers × Hidden
32 × 4096
Max Native Context
128k tokens
MoE Experts
N/A (Dense)
Quantization Memory Footprint
| Quantization | BPW | Weights Only | Total @ 8k Ctx | Total @ 32k Ctx | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 15.26 GiB | 17.57 GiB | 20.57 GiB | Lossless Reference |
| Q8_0 | 8.50 | 8.10 GiB | 10.42 GiB | 13.42 GiB | Near-Lossless |
| Q6_K | 6.57 | 6.26 GiB | 8.58 GiB | 11.58 GiB | High Precision |
| Q5_K_M | 5.69 | 5.43 GiB | 7.74 GiB | 10.74 GiB | Balanced High |
| Q4_K_M | 4.85 | 4.62 GiB | 6.94 GiB | 9.94 GiB | Recommended 4-bit |
| MLX_4BIT | 4.50 | 4.29 GiB | 6.60 GiB | 9.60 GiB | Apple Silicon Optimized |
| IQ4_XS | 4.25 | 4.05 GiB | 6.36 GiB | 9.36 GiB | Compact 4-bit |
| Q3_K_M | 3.91 | 3.73 GiB | 6.04 GiB | 9.04 GiB | Aggressive Compression |
Hardware Compatibility & Speed Leaderboard (Q4_K_M @ 32k Context)
| Hardware | Status | Total / Limit | Decode | TTFT (4k) | Max Safe Ctx | Inspect |
|---|---|---|---|---|---|---|
| Cloud NVIDIA H200 SXM (141 GB) | COMFORTABLE | 9.9 / 140.4 GiB | 435.4 tok/s | 0.13s | 128k | Simulate → |
| Cloud NVIDIA H100 SXM (80 GB) | COMFORTABLE | 9.9 / 79.4 GiB | 303.9 tok/s | 0.13s | 128k | Simulate → |
| Cloud NVIDIA A100 SXM (80 GB) | COMFORTABLE | 9.9 / 79.4 GiB | 180.6 tok/s | 0.37s | 128k | Simulate → |
| NVIDIA RTX PRO 6000 Blackwell (96 GB) | COMFORTABLE | 9.9 / 95.0 GiB | 162.6 tok/s | 0.45s | 128k | Simulate → |
| NVIDIA RTX 5090 (32 GB) | COMFORTABLE | 9.9 / 31.0 GiB | 158.7 tok/s | 0.55s | 128k | Simulate → |
| NVIDIA RTX 4090 (24 GB) | COMFORTABLE | 9.9 / 23.0 GiB | 89.3 tok/s | 0.69s | 128k | Simulate → |
| NVIDIA RTX 5080 (16 GB) | COMFORTABLE | 9.9 / 15.0 GiB | 85.0 tok/s | 1.01s | 73k | Simulate → |
| NVIDIA RTX 3090 (24 GB Used) | COMFORTABLE | 9.9 / 23.0 GiB | 80.9 tok/s | 1.63s | 128k | Simulate → |
| NVIDIA RTX 5070 Ti (16 GB) | COMFORTABLE | 9.9 / 15.0 GiB | 79.3 tok/s | 1.28s | 73k | Simulate → |
| AMD Radeon RX 7900 XTX (24 GB) | COMFORTABLE | 9.9 / 23.0 GiB | 78.8 tok/s | 1.13s | 128k | Simulate → |
| Cloud NVIDIA L40S (48 GB) | COMFORTABLE | 9.9 / 47.4 GiB | 76.5 tok/s | 0.33s | 128k | Simulate → |
| Apple Mac Studio M3 Ultra (256 GB) | COMFORTABLE | 9.7 / 199.7 GiB | 69.0 tok/s | 2.12s | 128k | Simulate → |
| Apple Mac Studio M3 Ultra (512 GB) | COMFORTABLE | 9.7 / 399.4 GiB | 69.0 tok/s | 2.12s | 128k | Simulate → |
| 2x NVIDIA RTX 3090 Rig (48 GB) | COMFORTABLE | 9.9 / 46.6 GiB | 67.9 tok/s | 2.00s | 128k | Simulate → |
| Apple Mac Studio M2 Ultra (192 GB) | COMFORTABLE | 9.7 / 149.8 GiB | 67.4 tok/s | 2.34s | 128k | Simulate → |
| Apple Mac Studio M4 Max (64 GB) | COMFORTABLE | 9.7 / 49.9 GiB | 45.4 tok/s | 3.21s | 128k | Simulate → |
| Apple Mac Studio M4 Max (128 GB) | COMFORTABLE | 9.7 / 99.8 GiB | 45.4 tok/s | 3.21s | 128k | Simulate → |
| NVIDIA RTX 5060 Ti (16 GB) | COMFORTABLE | 9.9 / 15.0 GiB | 38.7 tok/s | 2.42s | 73k | Simulate → |
| Apple MacBook Pro M3 Max (64 GB) | COMFORTABLE | 9.7 / 49.9 GiB | 32.8 tok/s | 4.30s | 128k | Simulate → |
| Apple MacBook Pro M3 Max (128 GB) | COMFORTABLE | 9.7 / 99.8 GiB | 32.8 tok/s | 4.30s | 128k | Simulate → |
| Apple Mac Mini M4 Pro (48 GB) | COMFORTABLE | 9.7 / 37.4 GiB | 21.8 tok/s | 6.61s | 128k | Simulate → |
| Apple Mac Mini M4 Pro (64 GB) | COMFORTABLE | 9.7 / 49.9 GiB | 21.8 tok/s | 6.61s | 128k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (128 GB) | COMFORTABLE | 9.7 / 96.0 GiB | 20.5 tok/s | 2.40s | 128k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (64 GB) | COMFORTABLE | 9.7 / 48.0 GiB | 20.5 tok/s | 2.40s | 128k | Simulate → |
| Apple Mac Mini M4 (24 GB) | COMFORTABLE | 9.7 / 18.0 GiB | 9.3 tok/s | 14.61s | 110k | Simulate → |
| Ryzen AI 9 HX 370 Mini-PC (64 GB) | COMFORTABLE | 9.7 / 48.0 GiB | 9.1 tok/s | 6.82s | 128k | Simulate → |
| NVIDIA RTX 3060 (12 GB) | TIGHT | 9.9 / 11.0 GiB | 30.3 tok/s | 4.62s | 41k | Simulate → |
| Apple Mac Mini M4 (16 GB) | TIGHT | 9.7 / 10.0 GiB | 9.3 tok/s | 14.61s | 46k | Simulate → |
Interactive Llama 3.1 8B Instruct Simulator
8.03B Dense · GQA
1792 GB/s · 250 TFLOPS
3. Weight Quantization
4.85 bpw
4. KV Cache
32,768 tok
Compute Roofline
COMFORTABLE FIT Memory-Bandwidth Bound
Max Safe Ctx: 128k
Memory Allocation Footprint 9.94 / 95.00 GiB usable
Weights: 4.62 GiB
KV Cache: 4.00 GiB
Overhead: 1.31 GiB
Headroom: 85.06 GiB
Decode Speed
162.6
tok/s (batch=1)
Prefill Speed
9922
prompt tok/s
TTFT Latency
0.45s
for 4,096 tok
Active Params
8.0B
of 8.03B total
llama.cpp Server Command
llama-server -hf meta-llama/Llama-3.1-8B-Instruct:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080| Context | KV Cache | Total VRAM | Decode | Status |
|---|---|---|---|---|
| 4k | 0.50 GiB | 6.16 GiB | 273.6 tok/s | COMFORTABLE |
| 8k | 1.00 GiB | 6.94 GiB | 249.3 tok/s | COMFORTABLE |
| 16k | 2.00 GiB | 7.94 GiB | 211.6 tok/s | COMFORTABLE |
| 32k | 4.00 GiB | 9.94 GiB | 162.6 tok/s | COMFORTABLE |
| 64k | 8.00 GiB | 13.94 GiB | 111.0 tok/s | COMFORTABLE |
| 128k | 16.00 GiB | 21.94 GiB | 68.0 tok/s | COMFORTABLE |
Buy-vs-Cloud-vs-API Monthly TCO Break-Even
USE SERVERLESS APIOwn Hardware/mo
$162.07
27.0h active + power
Cloud GPU/mo
$57.51
@$1.85/hr spot/pod
Serverless API/mo
$1.60
API cheaper
Recommended Hardware & Cloud Deployment
Contextual to fit statusEngineering FAQ — Llama 3.1 8B Instruct
How much VRAM does Llama 3.1 8B Instruct require in Q4_K_M?
Llama 3.1 8B Instruct weights occupy 4.62 GiB in Q4_K_M. Including 32k FP16 KV cache and runtime buffers, total memory is 9.94 GiB.
What is the cheapest local hardware that runs Llama 3.1 8B Instruct?
NVIDIA RTX PRO 6000 Blackwell (96 GB) (96 GB) runs Llama 3.1 8B Instruct Q4_K_M at ~162.6 tok/s decode and 0.45s TTFT.
How does Llama 3.1 8B Instruct scale KV cache at long context?
Llama 3.1 8B Instruct uses GQA with 8 KV heads across 32 layers (128 head_dim).