LLaMA 3 8B on RTX 4060 Ti: Performance Benchmark & Cost, Category: Benchmarks, Slug: llama-3-8b-on-rtx-4060-ti-benchmark, Excerpt: LLaMA 3 8B benchmarked on RTX 4060 Ti: 28 tok/s at FP16, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

There is something satisfying about running LLaMA 3 8B at full FP16 precision without any quantisation compromises. The RTX 4060 Ti and its 16GB of VRAM make that possible — barely. With 28 tokens per second at native precision, this GPU occupies a unique position in the lineup: it is the most affordable card that lets you skip quantisation entirely for an 8B model. Here is what that looks like in practice on GigaGPU dedicated servers.

Full Precision, Full Speed

Benchmark conditions: single-stream generation, 512-token prompt, 256-token completion, llama.cpp or vLLM backend. GGUF Q4_K_M via llama.cpp or vLLM FP16.

Running FP16 instead of 4-bit quantisation means you preserve the full quality of LLaMA 3’s outputs. This matters most for tasks like code generation and complex reasoning where quantisation artifacts can degrade accuracy. The 28 tok/s rate feels responsive in chat, and at batch size 8, the card pushes 44.8 tok/s — enough to serve a small API.

The Tight Memory Situation

Here is the catch: the FP16 weights alone consume 16.8 GB, which actually exceeds the 4060 Ti’s 16 GB frame buffer. In practice, the runtime uses layer offloading and careful memory management to make it work, but you are operating right at the edge. There is zero headroom for anything else. If you need longer context or concurrent requests, you should either drop to 4-bit quantisation (which frees about 11 GB) or step up to the RTX 3090 with its 24 GB buffer.

Price Per Token

Despite costing £99/month — double the RTX 3050 — the 4060 Ti delivers substantially better cost efficiency at £4.96 per million tokens. Batched inference drops that to roughly £3.10. For teams that care about output quality and cannot tolerate quantisation loss, this is the cheapest path to full-precision LLaMA 3. Cross-reference these numbers on our benchmark comparison tool.

Best Use Cases

Pick the 4060 Ti when output quality is non-negotiable but your budget does not stretch to the RTX 3090. It handles development, testing, and light production traffic well. Just be aware that the zero-headroom memory situation means you should monitor VRAM usage closely and avoid pushing beyond 8K context.

Quick deploy:

docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/llama-3-8b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99

Explore our LLaMA hosting guide, read the best GPU for LLaMA roundup, or compare against DeepSeek 7B on RTX 4060 Ti. All results are available on our benchmarks page.