LLaMA 3 8B on RTX 3050: Performance Benchmark & Cost, Category: Benchmarks, Slug: llama-3-8b-on-rtx-3050-benchmark, Excerpt: LLaMA 3 8B benchmarked on RTX 3050: 8 tok/s at 4-bit GGUF Q4_K_M, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

Can you actually run Meta’s LLaMA 3 8B on an entry-level GPU with just 6GB of VRAM? The short answer is yes — but only if you are willing to make some compromises. We squeezed this 8-billion-parameter model onto the NVIDIA RTX 3050 using aggressive 4-bit quantisation, and the results tell an interesting story about what budget hardware can realistically handle for LLM inference on GigaGPU dedicated servers.

Benchmark Results

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.

Eight tokens per second is roughly the pace of a slow human typist. For interactive chat that is borderline usable — you will notice the delay, but it will not feel broken. The real limitation shows up when you try to batch requests. Even at batch size 8, throughput barely climbs to 10.4 tok/s because the 3050’s 96 GB/s memory bandwidth simply cannot feed data to the compute units fast enough.

Why VRAM Is the Constraint Here

With only 0.5 GB of headroom after loading the model, there is virtually no room for longer contexts or concurrent users. You are locked to 4K context, and even that is tight. Attempting to push beyond it will cause out-of-memory crashes. This is strictly a single-user, short-context setup.

What It Costs

At £8.681 per million tokens in single-stream mode, this is not the cheapest way to run LLaMA 3 8B. Batching brings it down to roughly £5.43 per 1M tokens, which starts to look more reasonable. Still, the RTX 3050 at £49/month undercuts API providers charging £0.50-2.00+ per million tokens the moment you have even modest daily usage. Compare this against other GPUs on our tokens-per-second benchmark to see where the 3050 falls in the lineup.

Who Should Consider This Setup

The RTX 3050 running LLaMA 3 8B makes sense in exactly two scenarios: prototyping a new application before committing to pricier hardware, or running a personal assistant where speed is not critical. For anything resembling production traffic, you will want to step up to at least an RTX 4060 for a meaningful jump in throughput.

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

Read our full LLaMA hosting guide for setup details, or see our best GPU for LLaMA comparison. You might also want to check the DeepSeek 7B on RTX 3050 benchmark for an alternative model on the same hardware, or browse all benchmark results.