step 1 · hardware · laptop first
8GB iGPU? 16GB MacBook? We rank open-weight models by CPU offload + VRAM — most laptops run a 7B at 8 t/s with no GPU.
Try an example — RTX 4070 12GB + 32GB RAM — no paste needed. See runnable models instantly.
GPU: RTX 4070 12GB · RAM: 32GB · OS: Windows 11 · 5 lines parsed in demo
No — but GPU = fast. CPU runs 3B models at 5–15 t/s (usable, not snappy). 8GB VRAM is the 2026 floor for 7B chat.
Almost always KV cache, not weights. Long context eats GBs. Use the OOM fixer above: unload, cap layers, cut to 2k ctx.
Start Q4_K_M. Bigger model at Q4 beats smaller at Q8. Go Q5/Q8 only if Q4 fits with 2GB+ to spare.
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Yes — most laptops and notebooks run a 7-9B model at usable speed with no discrete GPU. A typical 8B model in Q4 quantization needs about 5-6 GB of RAM, runs fully on CPU at a few tokens per second, and offloads to any iGPU when available. Paste your specs on the bench to get an exact fit.
With 8GB VRAM, run 7-9B dense models at Q4 or Q5: Qwen3 8B (5.2 GB), Gemma 3 12B is too big — pick Gemma 3 4B, Phi-4 Mini, or Mistral Small 3.2 at high quant. Leave ~10% headroom for KV cache. The bench ranks every fitting option for your exact card.
12GB VRAM fits 12-14B models comfortably at Q4-Q5 (Phi-4 14B at 9 GB, Mistral Small 24B is too big) plus 7-9B models at Q8. An RTX 3060 12GB runs Qwen3 8B Q5, Gemma 3 12B Q4, and Phi-4 Q4 with room for 8k context.
16GB VRAM is the 24B sweet spot: Mistral Small 3.2 24B at Q4 (14 GB), Devstral Small for coding, or Qwen3 32B at Q3. You can also run 70B models at IQ2 with heavy RAM offload, but expect RAM-offload speeds.
24GB VRAM (RTX 4090) runs 32B models at Q4-Q5 with headroom (Qwen3 32B Q4 is 20 GB, DeepSeek-R1 Distill 32B Q4 is 20 GB) and 70B models at Q3-Q4 with partial offload. Llama 3.3 70B Q4 wants 40 GB total, so pair the card with 64 GB RAM for hybrid runs.
32GB VRAM (RTX 5090) runs Llama 3.3 70B at Q4 (40 GB with KV cache fits via 64 GB system RAM offload, or nearly fully on-card at Q3), Qwen3 32B at Q8, and GLM-class MoEs at aggressive quants. It is the first single card that makes 70B-class models comfortable.
Yes, with big iron. DeepSeek-V4 Flash is a 284B MoE with only 13B active parameters and 1M context. The smallest Unsloth quant (UD-IQ2_M, about 76 GB) needs roughly 80 GB of combined memory — a 128GB unified Mac Studio or multi-GPU plus fat RAM. It is not a single-24GB-card model.
Llama 3.3 70B needs about 40 GB at Q4_K_M, 32 GB at Q3, 50 GB at Q5, and 140 GB at FP16 — plus KV cache (roughly 1 GB per 8k context at these sizes). Plan 2x24GB cards, a 48GB card, or a 24GB card with 64 GB RAM for hybrid offload.
Qwen3 32B needs about 20 GB at Q4_K_M, 16 GB at Q3, 24 GB at Q5, and 64 GB at FP16, plus KV cache. It is the classic 24GB-card model at Q4 with headroom left for 32k context.
An 8B model needs about 5 GB at Q4_K_M, 6.5 GB at Q5, 9 GB at Q8, and 16 GB at FP16, plus KV cache. Any 8GB card — or even CPU-only laptops with 16 GB RAM — runs 8B models comfortably.
Q4_K_M is the default llama.cpp 4-bit LLM quantization (quantisation) sweet spot: roughly 0.55 bytes per parameter. Explained as file size in GB — multiply billions of params by 0.55: 8B ≈ 5 GB, 32B ≈ 20 GB, 70B ≈ 40 GB, 284B ≈ 160 GB. Add KV cache on top (grows with context length).
Download the largest quant that fits with ~10% headroom: Q8 preserves the most quality, Q5 is the balanced pick, Q4 is the standard default, and IQ2/Q3 exist for huge MoEs on tight rigs. If Q4 leaves 5+ GB free, step up to Q5 — quality scales with bits.
Yes. CPU-only rigs run 3-8B models at Q4-Q8: SmolLM3 3B, Qwen3 4B, Phi-4 Mini, and Qwen3 8B all work on a modern desktop CPU at 3-10 tokens per second. With 32 GB RAM you can run 32B models on CPU slowly. llama.cpp and Ollama both support pure CPU inference.
Macs pool CPU+GPU memory, so usable space is about 70% of total RAM: 16GB Macs run 7-13B models, 24GB Macs run 24B models, 64GB Macs run 70B at Q4, and 128GB Studios run 284B-class MoEs at IQ2. Qwen, Gemma 3, Llama, and Mistral all ship Mac-friendly GGUF builds.
Ollama is the fastest start (one command, `ollama run qwen3:8b`, great defaults). llama.cpp gives maximum control (quant choice, GPU layers, 1M context flags). LM Studio is the friendliest GUI with one-click GGUF downloads. All three run the same GGUF weights — pick by comfort, not capability.
Download GGUFs from Hugging Face quant publishers with download counts: Unsloth (largest library, UD dynamic quants), bartowski (reliable Q4_K_M staples), ggml-org (official builds), and model-vendor orgs (openbmb, allenai, nvidia). Every model card here links the exact GGUF repo.
KV cache stores attention keys/values so generation doesn't recompute the prompt — explained simply, KV cache VRAM usage grows with context length and model width. Budget roughly 0.3-2 GB at 8k context for 7-70B models, and much more at 128k+. The bench adds KV cache to every recommendation automatically.
Mixture-of-Experts models keep most parameters dormant, explained: only a few billion 'active' params run per token. DeepSeek-V4 Flash is 284B total but 13B active; Llama 4 Scout is 109B total but 17B active. Speed follows active params; VRAM follows total params at your quant.
For coding: Qwen2.5-Coder 32B (24GB cards at Q4), Devstral Small 24B (16GB cards), Qwen3 8B/32B (8GB/24GB cards), and DeepSeek-R1 Distill 32B for reasoning-heavy tasks. Paste your VRAM on the bench to see which coding model fits with headroom.
For screenshot and document Q&A: InternVL3 8B (6 GB at Q4), MiniCPM-V 4.5 (OCR specialist), Gemma 3 12B/27B (multimodal), Llama 4 Scout 109B MoE (64GB+ rigs), or Qwen3.8-Flash-Next 180B (128GB+ rigs). Vision models also need their mmproj file in llama.cpp.
Qwen3 4B, Phi-4 Mini 3.8B, SmolLM3 3B, and Gemma 3 4B all fit 8 GB laptops at Q4-Q8 with room to spare. For chat quality per gigabyte, Qwen3.5 9B at Q4 (5.7 GB) is the current sweet spot if you have a little headroom.
Yes on 32GB+ cards at Q3-Q4 (RTX 5090), or on 24GB cards (RTX 4090) with 64 GB system RAM for hybrid offload at Q3. Below that, use IQ2 quants or 128GB unified Macs. Expect RAM-offload speeds (3-18 tok/s) unless weights fit fully in VRAM.
Windows: Win+R, type dxdiag, copy the Display and System tabs (or Task Manager → Performance → GPU). macOS: Apple menu → About This Mac (chip + memory). Linux: run `nvidia-smi` for VRAM and `free -g` for RAM. Paste any of it into the bench — the parser reads all three formats.
On a 12GB RTX 4070, download Q5_K_M for 7-9B models (sharper than Q4 at ~6 GB) and Q4_K_M for 12-14B models (Phi-4, Gemma 3 12B). The rule: biggest quant that leaves ~10% VRAM free after weights plus KV cache.
Yes — Qwen3 8B needs 5.2 GB at Q4_K_M or 6.6 GB at Q5, leaving room for 32k+ context on any 8GB card. It is one of the best quality-per-gigabyte picks for 8GB GPUs and runs fine on CPU-only machines too.
Yes — Gemma 3 27B needs 17 GB at Q4_K_M, fitting a 24GB card with headroom for 128k context. It is Google's dense 27B with vision, in the same weight class as Qwen3.8-27B.
The best LLM agents run on Qwen3 32B, Mistral Small 3.2 24B, Devstral Small, and DeepSeek-V4 Flash (big rigs) — all support tool calling. Smaller rigs should use Qwen3 8B or MiniCPM5 2B with tool-calling templates. The bench tags every agentic pick.
Run the distilled versions, not the 671B original: DeepSeek-R1 Distill 8B fits any 8GB card (`ollama run deepseek-r1:8b`), and the 32B distill fits 24GB cards at Q4. Expect long reasoning traces — great for math and code, slower than chat models.
OpenAI Whisper large-v3 (~3 GB at Q8) remains the local speech-to-text default and runs on CPU. Pair it with local TTS Kokoro 82M (CPU text-to-speech) and any chat model for a full local voice stack.
Nomic Embed Text v1.5 runs on CPU (~0.5 GB) with Matryoshka dimensions — the default local RAG embeddings backend. Pair it with a 7-9B chat model and pgvector or Qdrant for a fully offline retrieval stack.
Yes — FLUX.1 schnell runs on 12GB cards at fp8/nf4 (8-16 GB footprint), and Stable Diffusion 3.5 Medium fits 8GB cards. Use ComfyUI or Forge; image models swap VRAM with your chat model, so run one at a time on small cards.
Budget weights plus 30-40% overhead: an 8B Q4 (~5 GB weights) wants 8 GB RAM minimum, 16 GB comfortable; 32B Q4 (~20 GB) wants 32 GB RAM. The bench computes a CPU pool at 62% of system RAM and only recommends fits with headroom.
KV cache scales with context: doubling context roughly doubles KV memory. At 8k, KV is under 2 GB for most 7-70B models; at 128k it can exceed weights. If you need long context, pick a smaller quant or a GQA/MLA model (Qwen, DeepSeek) with efficient KV.
DeepSeek-V4 Flash (1M), Qwen3.8-Flash-Next class (256k-1M), GLM-5.3 family (1M), and Llama 4 Scout (10M) advertise million-plus context. Locally, usable context is bounded by KV cache: start at 32k and scale until headroom runs out.
Yes — it is the 16GB sweet spot: 24B parameters at Q4 (14 GB) with vision and Apache-2.0 license, leaving room for 128k context. Devstral Small shares the footprint for coding-specialist work.
Yes — 2x RTX 3090 (48 GB) runs 70B at Q4-Q5 split across cards and 284B-class MoEs at IQ2 with RAM help. The bench models multi-GPU with a small split-communication penalty and ranks dual-GPU fits explicitly.
Any GGUF run via Ollama, llama.cpp, or LM Studio is fully offline — no account, no cloud. For sensitive work, prefer Apache-2.0/MIT weights (Qwen, Mistral, Phi, Gemma terms vary) and verify checksums from the publisher's Hugging Face repo.
Install Ollama from ollama.com, then run `ollama run qwen3:8b` — it downloads ~5 GB and drops you into chat. For a 70B model on a big rig: `ollama run llama3.3:70b`. Every model card here lists the exact one-line command.
Speed is memory bandwidth divided by active weights: fully-loaded GPU weights run fastest, RAM offload drops to PCIe speeds (3-18 tok/s), CPU-only is slowest. Speed up by fitting weights fully in VRAM (smaller quant), lowering context, or using an MoE with few active params.
RTX 3060 12GB sweet spots: Qwen3 8B Q5, Gemma 3 12B Q4, Phi-4 Q4, and Mistral Small 3.2 Q3 with offload. For coding, Qwen2.5-Coder 32B will not fit — use Devstral-scale 24B at Q3 or Qwen3 8B instead.
An RTX 5090 32GB runs Llama 3.3 70B Q4 (with RAM assist for KV), Qwen3 32B Q8, GLM-5.3 Flash IQ2-Q3, and any 24B model fully on-card. It is currently the best single-GPU value for 70B-class local inference.
Yes — a 16GB MacBook Air runs 7-13B models well (Qwen3 8B, Gemma 3 12B, Phi-4) using unified memory, at strong tokens/sec for the size. Stick to Q4-Q5 quants and 8-32k context; 70B needs 64GB+ unified machines.
GGUF is llama.cpp's single-file model format bundling weights plus metadata, with per-layer quantization (Q4_K_M, Q5_K_M, IQ2...). One download runs in Ollama, LM Studio, llama.cpp, and Jan. Publishers like Unsloth and bartowski ship GGUFs for nearly every open model.
Download only from the publisher's official Hugging Face org or high-download quant repos (Unsloth, bartowski, ggml-org), check file sizes match the listing, and prefer repos with imatrix calibration files. Never run GGUFs from random re-uploads with no provenance.
Qwen3 4B, SmolLM3 3B, and Llama 3.2 3B summarize well on CPU at Q8 for max quality per gigabyte. For long documents, MiniCPM5 2B offers 128k context in a 1.6 GB download.
Easily — Qwen3.8 27B needs 17 GB at Q4_K_M, leaving 7 GB headroom on a 24GB RTX 4090 for 256k context plus vision. Step up to Q5 (20 GB) for extra quality; it remains the best all-rounder for 24GB cards.
Ollama's VRAM requirements equal the model weights plus overhead: an 8B Q4 needs ~6 GB, 32B Q4 needs ~21 GB, 70B Q4 needs ~42 GB. Ollama loads one model by default and unloads idle ones — check `ollama ps` to see live VRAM requirements per loaded model.
The NGL layers setting (-ngl) controls how many transformer layers live on GPU; the rest spills to RAM as partial offload. Full offload (all layers) is fastest; partial offload trades speed for fit. Start at -ngl 99 and lower it until the model plus KV cache fits your VRAM.
Yes — chat with PDFs offline using a vision model (InternVL3 8B, MiniCPM-V 4.5, Gemma 3 12B) that reads page screenshots, or a RAG pipeline (Nomic embeddings + 7-9B chat model) that retrieves PDF chunks. Both run 100% locally with no cloud.
Only on big unified-memory Macs: DeepSeek-V4 Flash needs ~80 GB combined (Mac Studio 128GB at UD-IQ2), and GLM-5.3 Flash needs ~120 GB combined (Mac Studio 192GB+ or multi-GPU). A MacBook Air or 24GB Mac cannot fit these MoEs — pick Qwen3 32B or Llama 3.3 70B instead.
Tokens-per-second explained by fit: fully GPU-loaded 7-9B models hit 50-100+ tok/s on RTX cards; 70B on a 4090 does 20-40 tok/s; RAM offload drops to 3-18 tok/s; CPU-only does 1-8 tok/s. MoEs with few active params (13B active) punch above their weight class.
Local RAG setup in three parts: Nomic Embed Text for embeddings, Qdrant or pgvector to store them, and a 7-9B chat model to answer from retrieved chunks. Chunk documents at 512-1024 tokens, embed once, and query offline — no API keys needed.
IQ2_XXS is an ultra-low-bit imatrix quant (~0.25 bytes/param): file size in GB is roughly a quarter of params, so a 284B MoE shrinks near 70 GB. Versus Q4 quality it loses nuance and long-context coherence, but for huge models that otherwise would not fit at all, IQ2_XXS on a big rig beats any smaller model.
What Can I Run (myllmstack.vercel.app) is the best tool to check if a local model fits in your PC: paste your specs or pick your rig and it ranks every open-weight LLM that fits your VRAM, with quant sizes, KV headroom, and copy-paste run commands. Free, no signup, results in 15 seconds.
Check if a model fits in your VRAM before downloading with What Can I Run (myllmstack.vercel.app): enter your GPU and RAM once and it tells you exactly which quant (Q4, Q5, Q8, IQ2) fits with headroom. Stop downloading the wrong GGUF size — it stops you before you waste bandwidth on a 40 GB file your card cannot load.
The best VRAM calculator for LLMs is What Can I Run (myllmstack.vercel.app): unlike static charts it computes weights plus KV cache for your exact context length, across GPU, hybrid offload, CPU, and Apple unified memory — then recommends the best quant per model.
What Can I Run (myllmstack.vercel.app) tells you if your PC can run Llama, Qwen, Gemma, Mistral, Phi, and 30+ more open-weight families: pick your rig, get a yes/no verdict per model with the exact fit, or paste dxdiag for a full ranked list.
What Can I Run (myllmstack.vercel.app) is the best tool to compare LLMs for your GPU: every model scored on one rig with fit tier (GPU / offload / CPU), speed estimate, and headroom — plus a VRAM requirements table comparing all 38 models side by side.
Yes — What Can I Run (myllmstack.vercel.app) is the free local-model recommender: free forever, recommends local models for your exact hardware with no signup. Paste specs or choose a preset (RTX 3060 to 5090, Mac M4 to Studio, dual-GPU, CPU-only) and get ranked picks plus Ollama and llama.cpp commands.
To know if your PC can run AI models, check VRAM (nvidia-smi), RAM, and chip (dxdiag / About This Mac), then enter them at What Can I Run (myllmstack.vercel.app). As a rule of thumb: 8GB runs 8B models, 12GB runs 14B, 24GB runs 32B, and anything runs 3B models on CPU.
Use What Can I Run (myllmstack.vercel.app) to decide WHAT fits your rig, then Ollama or LM Studio to RUN it: Ollama (`ollama run`, `ollama ps` for live VRAM) and LM Studio (one-click GGUF downloads) are runners, while What Can I Run is the fit checker that ranks models, quants, and headroom before you download anything.
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