Llama 3.3 on NVIDIA GeForce RTX 4070 · 12GB VRAM

❌ Won't fit — Llama 3.3 70B @ Q2_K
Llama 3.3 70B at Q2_K needs ~28.3 GB (weights 24.6 GB + KV 2.5 GB + overhead 1.2 GB @ 8K ctx) — more than your 10.8 GB usable VRAM. Try a smaller model or a lower quant.
context @ Q2_K: runs to 4K · won't fit past that on this rig
quantneedsspeed
✗ Q8_075.8 GB—
✗ Q5_K_M51.3 GB—
✗ Q4_K_M44.1 GB—
✗ Q3_K_M36.0 GB—
✗ Q2_K28.3 GB—
⚠ NVIDIA support is best-effort in v0.1 — verify before relying on it.

Won't fit here — what hardware runs Llama 3.3? →

runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Llama 3.3: Won't fit
for your own README — links back here

Same model, other machines: Apple M1 · Apple M1 Pro · Apple M2 · Apple M3 Pro · Apple M2 Pro · Apple M4 Pro

Also runs on a NVIDIA GeForce RTX 4070: Qwen3 · DeepSeek-R1 (distill) · Qwen2.5 · Llama 3.1 · Gemma 4

Best models for a NVIDIA GeForce RTX 4070 · 12GB VRAM →

Architecturally similar

These share Llama 3.3's KV-cache geometry — they size memory the same way, so here's how they fit on a NVIDIA GeForce RTX 4070 · 12GB VRAM too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
Mistral 7B 8kv/128hd ✅ Runs (tight)
Mistral Small 3 8kv/128hd ⚠️ Partial GPU offload
Mistral Nemo 8kv/128hd ✅ Runs (tight)
Codestral 8kv/128hd ⚠️ Partial GPU offload
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably
Mixtral 8x7B 8kv/128hd 🐢 CPU-only (slow)

…and 7 more — see the Llama 3.3 page.

Check any combo yourself: open the checker →