TechCompare LogoTechCompare

How much VRAM does Llama 3.1 8B need at Q4_K_M? Single GPU local inference

If you have a working PC, you can run Llama 3.1 8B locally. Q4_K_M is the recommended quant - it fits everywhere and the quality loss vs FP16 is negligible at this scale. Step up to Q8_0 only if you have headroom and want maximum quality.

Llama 3.1 8B at Q4_K_M needs about 23.8 GB of VRAM at its native 128K context. This is what makes local inference of small models at high context highly attractive on 24 GB consumer GPUs.

By TechCompare · Updated

Total VRAM required
23.8 GB
Llama 3.1 8B at Q4_K_M
Weights
4.5 GB
8B params
KV cache
17.2 GB
128K tokens, FP16 KV

Calculator

Estimated VRAM required

23.8 GB

8B params at Q4_K_M, 131,072 token context, batch 1, inference.

Weights
4.5 GB
KV cache
17.2 GB
Overhead
2.2 GB

Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.

KV cache exceeds model weights: Consider lowering the context length to save on VRAM. Contexts between 8K and 64K are generally more typical for local setups.

Hardware that fits

Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.

RTX 3090
Consumer
24 GB
99% used
RTX 5090
Consumer
32 GB
74% used
A100 40GB
Datacenter
40 GB
60% used
Apple M3 Max 64GB
Unified
~56 GB
43% of estimate
Same 8B @ Q4_K_M, 131,072 context scenario, pre-loaded. Change the quant, shrink the context, or try a different preset.
Edit in the full tool

How this is calculated

8B at Q4_K_M is roughly 4.5 GB of weights plus 17.2 GB of KV cache and 2.1 GB of activation overhead, totaling 23.8 GB.

Verdict

The 23.8 GB total splits as 4.5 GB of Q4 weights, 17.2 GB of FP16 KV cache at the full 128K context (8 KV heads at head_dim 128 across 32 layers), and 2.1 GB of activation overhead. The headline insight is that the cache exceeds the weights at this context setting: the model itself is small, but the long-context budget isn't. Cap to 32K and the cache collapses to about 4.3 GB, dropping the total to about 11 GB, which fits any 12 GB consumer card with headroom. So the budget question isn't whether Llama 3.1 8B fits your hardware, it's how long your context is Q8 KV halves the cache and is a free quality lever when you need a few extra tokens in the budget.

More Llama scenarios

MiMo-V2.6-Pro at Q4_K_M
Budget a 1.02T expert pool, hybrid attention, and a 1M text context.
View details ➜
MiMo-V2.6-Flash at Q4_K_M
309B total weights and separate global and sliding cache widths.
View details ➜
GLM-5.3-Flash at Q4_K_M
Model the 34 KDA layers, 11 sparse layers, and indexer storage.
View details ➜

Related guides

Context window explained
How a model context window caps prompt size.
View guide ➜

Frequently asked questions

Can I run Llama 3.1 8B on a 6 GB GPU?
Yes at reduced context lengths. At the native 128K context, the 17.2 GB KV cache exceeds standard consumer limits, but dropping context to 8K brings VRAM down to 6.1 GB.
Is Llama 3.1 8B good enough for daily use?
For chat, summarization, and simple code completion, yes. For complex reasoning, math, or production-quality writing, you'll notice the gap to 70B-class models. Use 8B for speed and 70B+ for accuracy.
What's the cheapest GPU that runs Llama 3.1 8B at Q4?
A 6 GB GPU works. Q4 weights are ~4.3 GB, KV cache at 32K context adds roughly 1 GB, overhead is 0.6 GB, totaling about 6 GB. The RTX 2060 6 GB and GTX 1660 Super 6 GB run it. Modern 6 GB cards can also do small batches. For 8K context, an 8 GB card is the comfortable floor.
How much does context length change the VRAM requirement for Llama 3.1 8B?
Roughly linearly. The KV cache at 8K context is about 0.25 GB with FP16 KV, and at 128K it grows 16x to about 4 GB, rivaling the 4.3 GB weight footprint. That's why the same card that runs 8B comfortably at short context starts to struggle at the model's maximum window, and why Q8 KV cache quantization is the standard fix for long-context work.

Related tools

RAM Latency Calculator

Convert DDR3/DDR4/DDR5 timings (CL, tRCD, tRP, tRAS) into true latency in nanoseconds.

Use tool ➜

Power Cost Estimator

Estimate annual electricity costs for your PC, Server, or TV.

Use tool ➜

Data Transfer Calculator

Estimate transfer times for files over USB, WiFi, Ethernet, and more.

Use tool ➜

Memory and Storage Throughput Visualizer

Visualize the massive speed difference between CPU cache, RAM, and storage.

Use tool ➜