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Best LLM for AMD 5700X + RTX 3070 Ti?

Quick Answer

With an RTX 3070 Ti (8 GB VRAM), Llama 3 8B Q4_K_M and Mistral 7B Q5_K_M are the best local LLMs. Both use ~6 GB VRAM and run at ~22–25 tok/s. The AMD Ryzen 7 5700X handles fast tokenization as a CPU fallback.

  • ▸Llama 3 8B Q4_K_M: ~6 GB VRAM, ~25 tok/s on RTX 3070 Ti
  • ▸Mistral 7B Q5_K_M: ~6 GB VRAM, strong reasoning per VRAM used
  • ▸RTX 3070 Ti has 8 GB VRAM — 13B models at Q4 may be too large

Updated: 2026-05

Hardware-SpecificIntermediate

Key Takeaways

  • ✓RTX 3070 Ti has 8 GB GDDR6X VRAM — Llama 3 8B Q4_K_M and Mistral 7B Q5_K_M run fully in VRAM at ~22–25 tok/s
  • ✓14B models at Q4_K_M need ~10 GB and do not fit; Q3_K_M (~7 GB) fits but quality drops noticeably
  • ✓The 5700X's 8-core Zen 3 design makes partial CPU offload viable for occasional 14B use at ~8 tok/s
  • ✓This rig handles most chat, Python, and TypeScript work — GPU is the bottleneck, not the CPU

What Runs Well on This Rig

As of May 2026, the RTX 3070 Ti (8 GB GDDR6X, 608 GB/s bandwidth) runs Llama 3 8B Q4_K_M and Mistral 7B Q5_K_M fully in VRAM — approximately 6 GB each — at ~22–25 tok/s. The 14B model class is the hard ceiling: it needs ~10 GB at Q4, which exceeds the 8 GB limit.

If a 14B model is required, three paths exist: Q3_K_M drops the footprint to ~7 GB and fits entirely in VRAM, but degrades output quality on reasoning and code tasks. Partial CPU offload via llama.cpp (splitting layers between VRAM and RAM) is viable at ~8 tok/s — the 5700X's 8 Zen 3 cores handle this better than a 4-core CPU. Running a 70B model at Q2_K is technically possible at ~1 tok/s but not practical for interactive use.

If 14B coding models at full quality are the goal, see the best coding LLMs for 12 GB VRAM for the hardware upgrade path.

ModelSetupSpeed
Llama 3 8B Q4_K_MFull VRAM~25 tok/s
Mistral 7B Q5_K_MFull VRAM~22 tok/s
Qwen 14B Q3_K_MFull VRAM (tight)~14 tok/s (quality drop)
Qwen 14B Q4_K_MPartial CPU offload~8 tok/s
Llama 3 70B Q2_KCPU-heavy~1 tok/s (slow)

When to Upgrade or Stay

This rig runs 7B–8B models at 20+ tok/s — sufficient for general chat, Python scripting, TypeScript tooling, and single-file code review. If that describes your workload, there is no pressing reason to upgrade.

If you need 14B coding models without a quality or speed penalty, the GPU is the upgrade target — not the CPU. A used RTX 3060 12 GB (typically $200–$300) or RTX 4070 base (12 GB) unlocks Qwen 2.5 Coder 14B at Q4 at full throughput. The 5800X3D is the top AM4 CPU upgrade, but its 3D V-Cache benefit is specific to gaming and CPU-bound scientific workloads — LLM inference is GPU-memory-bandwidth-bound and the 5700X is not the bottleneck here.

For the full GPU selection guide and how memory bandwidth maps to LLM inference speed, see the best GPUs for local LLMs guide.

Quick Answers About LLMs for AMD 5700X + RTX 3070 Ti

Can I run a 14B model on RTX 3070 Ti 8 GB?▾
Not at Q4_K_M — 14B models need approximately 10 GB, which exceeds the 8 GB limit. Q3_K_M (~7 GB) fits but output quality drops noticeably on reasoning and code tasks. Partial CPU offload via llama.cpp is possible at ~8 tok/s.
Should I upgrade GPU or CPU for better LLM performance?▾
GPU. LLM inference speed is GPU-memory-bandwidth-bound; the 5700X is not the bottleneck. Upgrading to a 12 GB GPU (RTX 3060 12 GB or RTX 4070 base) unlocks the 14B model tier at full Q4 quality and speed.
Does RAM speed matter for partial CPU offload?▾
Yes, as a secondary factor. DDR4-3600 vs DDR4-2133 gives roughly 15% more CPU-offload throughput for the RAM-resident layers. The GPU remains the primary constraint for the layers that fit in VRAM.
Is the 5800X3D worth it over the 5700X for LLMs?▾
No. The 5800X3D's 3D V-Cache benefits gaming and certain CPU-bound workloads, but LLM inference is GPU-memory-bandwidth-bound. The 5700X is not the bottleneck on this rig — put the upgrade budget toward a 12 GB GPU instead.
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