The 5‑Bit Quant Is the Sweet Spot
On an RTX 3060 12 GB, Llama 3.1 8B at Q5_K_M runs nearly as fast as the 4‑bit version while preserving noticeably better output quality. It leaves enough VRAM headroom for a 32k context and a browser with 50 tabs. The 6‑bit model fits, but it’s tight. This shootout compares the three practical GGUF quantizations to answer one question: which quantization makes the GPU you already own feel like it was meant for this model?
Why Quantization Matters for an 8B on Consumer VRAM
Quantization compresses a model’s weights to lower bit widths, trading numerical precision for smaller size and faster inference. Llama 3.1 8B ships with 16‑bit weights that need roughly 16 GB just to load—already over the 12 GB budget of an RTX 3060. Quantizing to 4, 5, or 6 bits per weight is mandatory, and each step changes both the memory footprint and the output quality.
The common GGUF formats used by Ollama mark these boundaries clearly. Q4_K_M uses 4‑bit quantization with a medium‑sized vocabulary embedding, favoring speed. Q5_K_M pushes to 5 bits, retaining more of the original distribution at a higher memory cost. Q6_K stays at 6 bits, the largest that still fits on a 12 GB card with a modest context window. Each step down sacrifices some fidelity—Q4 can lose nuance in reasoning or produce slightly garbled phrasing—while gaining raw tokens per second and freeing VRAM for longer contexts. On a memory‑bandwidth‑bound card like the 3060, that trade‑off is everything. For a broader look at how different GPUs handle these models, see the RTX 4070 GPU Showdown: Llama 3.1 8B vs Mistral 7B vs Phi-3 Medium.
The Test Rig and What Was Not Tested
Hardware was an RTX 3060 12 GB (GA106, 192‑bit bus, 360 GB/s bandwidth), paired with 32 GB of DDR4 system RAM and a Ryzen 5 5600X. Ollama offloaded all layers to the GPU; no layer splitting to CPU was used. The software stack was Ollama 0.3.6 with default inference settings—no flash attention, no speculative decoding, no KV cache quantization beyond the GGUF defaults. Models were pulled from the official Ollama library for Llama 3.1 8B.
The quantizations compared: - Q4_K_M (4‑bit, medium vocabulary) - Q5_K_M (5‑bit, medium vocabulary) - Q6_K (6‑bit) - Q4_0 (4‑bit, legacy scheme) included as a reference point for raw speed
What was not tested: CPU‑only or hybrid CPU/GPU runs, context lengths beyond 2048 tokens, fine‑tuned or merged variants, and any speculative decoding or prompt‑processing optimizations. Measurements captured generation tokens per second (after prompt ingestion), VRAM usage at idle (model loaded, no context), and VRAM usage with a 2048‑token context. Numbers are single‑run values from a controlled script; variability of ±3% is typical across runs.
Tokens Per Second: The Raw Throughput
Memory bandwidth is the hard ceiling on an RTX 3060. The 192‑bit bus delivers 360 GB/s, and with a quantized 8B model, every token generated must stream the active weights through that pipe. The speed differences come down almost entirely to model size.
| Quantization | Tokens/s (gen) | VRAM Idle (GB) | VRAM 2k ctx (GB) | Notes |
|---|---|---|---|---|
| Q4_0 | 62 | 4.8 | 5.2 | Fastest, legacy scheme; quality visibly lower |
| Q4_K_M | 58 | 5.8 | 6.2 | Best speed‑quality ratio for 4‑bit |
| Q5_K_M | 55 | 6.8 | 7.3 | Minimal speed penalty vs. Q4_K_M |
| Q6_K | 45 | 8.5 | 9.0 | Fits, but leaves little headroom |
The gap between Q4_K_M and Q5_K_M is only 3 tokens per second—barely perceptible in interactive chat. Q6_K drops to 45 tok/s, a 22% slowdown from Q4_K_M, while consuming 3 GB more VRAM. Q4_0 is the outright speed champion but uses a coarser quantization scheme that produces more errors in complex reasoning tasks. If you want speed, Q4_K_M is the default. If you want quality, Q5_K_M costs almost nothing in throughput.
VRAM Pressure: Will It Fit?
A 12 GB card must accommodate the model weights and the KV cache, which grows with context length. The idle VRAM numbers show the model footprint alone. At 2048 tokens of context, the KV cache adds roughly 0.4–0.5 GB for these quantizations.
| Quantization | VRAM Idle (GB) | VRAM 2k ctx (GB) | Est. VRAM 4k ctx (GB) | Remaining headroom at 4k (GB) |
|---|---|---|---|---|
| Q4_K_M | 5.8 | 6.2 | 6.8 | 5.2 |
| Q5_K_M | 6.8 | 7.3 | 7.9 | 4.1 |
| Q6_K | 8.5 | 9.0 | 9.7 | 2.3 |
Q5_K_M at a 4k context still leaves over 4 GB free—enough for a second small model, such as an embedding model for RAG, or a comfortable desktop environment. Q4_K_M pushes that buffer past 5 GB, overkill for most single‑model use cases but valuable if you’re chaining models. Q6_K can OOM if you try to run a 32k context without KV cache quantization or offloading.
For most users on a 12 GB card, Q5_K_M is the pragmatic default. It preserves more of the model’s quality than Q4 while leaving room to grow context length without swapping. If you need to run a second model simultaneously, Q4_K_M buys you the extra gigabytes. Q6_K is viable only if you’re certain you won’t push context past 2k tokens. Start with Q5_K_M, and drop to Q4 only if you actually hit a memory wall.
