make-localllm-easier — run the best local LLM your GPU can handle, in one command

The leanest way to run local AI: least CPU, RAM and GPU memory, smallest files, measured quality.

localllm picks, downloads and runs the most accurate local AI model for your PC and your language, chosen from real benchmark measurements, with llama.cpp tuned for AMD, NVIDIA, Intel and Apple GPUs.

pip install make-localllm-easier
localllm

That's it. localllm checks your GPU and RAM, picks the most accurate model we have measured for your language that fits your card, downloads llama.cpp and the model, starts it with settings profiled op by op, and opens the chat page. You also get an OpenAI-compatible API at http://127.0.0.1:8080/v1 for any app that speaks it. Offline, private, free.

localllm chat       # chat right here in the terminal (Thai, Japanese, any language)
localllm doctor     # what this GPU is good for: model sizes, speed, how much text it can hold
localllm list       # every model we have measured, with scores per language
localllm serve      # API only (OpenAI, Anthropic, Ollama and Gemini formats), no browser
localllm route      # optional: mix in your own cloud key, compare local vs cloud
localllm eval       # score any running server in English + your language

FAQ

Which local LLM should I run on my GPU? Run localllm doctor. It lists which model sizes fit your card (4B up to 120B MoE), at which quantization, how fast they should run, the most accurate measured model for your language, and how much system RAM that model uses (est.).

Can a 16 GB GPU run a 27B model? Yes. Qwen3.8-27B at ~3.5 bits (12.2 GB) runs at ~50 tok/s on an RX 9070 XT and keeps 81.8% on English Global-MMLU-Lite. gemma-4-26B-A4B (13.3 GB) runs at ~85 tok/s with similar accuracy.

Is a 2-bit quantized model good enough? Usually not for non-English use: 2-bit costs 8-13 accuracy points, and Hindi, Arabic and Thai lose the most (13 points).

Can it use a different local model for each message? Yes, opt-in: localllm serve --models auto routes each message to the model with the best measured score for its language and task (e.g. Chinese knowledge to Qwen3.8, Chinese math to gemma-4) and says which model answered. On a 16 GB card a swap costs 4-9 s, so it only switches for a gain of 3+ points; on 24 GB+ cards both models stay loaded and routing is instant.

Which local model is better at math? On MGSM (the same 250 word problems in 11 languages), gemma-4-26B-A4B QAT scores 96.8 / 89.6 / 88.8 in English / Thai / Chinese vs 94.4 / 87.2 / 84.4 for Qwen3.8-27B Q3, at 1.8x the speed. Measure any server yourself with localllm eval --suites math --langs en,de,ja.

Why is llama.cpp slow on my AMD (or Intel) GPU on Windows? If Resizable BAR is off, llama.cpp's Vulkan backend puts buffers in a 256 MB host-visible heap backed by system RAM and decode drops up to 1.7x. localllm sets GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1 for you (llama.cpp#27097).

Can I chat with a local LLM in the terminal? Yes: localllm chat. Answers stream as they're written, the conversation is remembered, /save writes it to a file, /think shows the model's reasoning, Ctrl+C stops an answer.

Can I use it as an Ollama, OpenAI, Anthropic or Gemini replacement? Yes. One local endpoint at http://127.0.0.1:8080 speaks all four APIs, so existing apps and SDKs only need a new base URL. See docs/apis.md.

Can it fall back to my cloud API key? Only if you turn it on. Routing is off by default; with your own key in an environment variable it sends a request to the cloud only when a rule says so (prompt too long, a cloud model asked for by name, or the local model scores below your floor in that language) and tells you where each answer came from.

How much RAM does a local LLM need? With the model fully on the GPU, about 2-2.5 GB of system RAM with localllm (vs ~9 GB growing with llama-server's defaults). localllm doctor prints the estimate for your PC.

Does it work offline? After the first download, yes. Nothing leaves your PC.

Which languages are measured? 23 languages on Global-MMLU-Lite, 44 countries' own exams on INCLUDE, plus Thai (ThaiExam). localllm eval --langs ... measures any of them on your hardware.

What localllm doctor tells you

GPU AMD Radeon RX 9070 XT  15.9 GB  (640 GB/s)    RAM 32 GB    language: th

Model sizes for this PC (whole model on the GPU = fast):
  [OK  ] 4B                       Q8 4.2 GB  ~90 tok/s (est.)
  [OK  ] 8B                       Q8 8.5 GB  ~45 tok/s (est.)
  [OK  ] 14B                      Q6 11.5 GB  ~33 tok/s (est.)
  [OK  ] 24-32B                   Q3 13.2 GB  ~29 tok/s (est.)
  [SLOW] 30B MoE (3B active)      Q4 18.0 GB with experts in RAM - works, ~10-25 tok/s
  [NO  ] 70B                      needs ~42.0 GB - too big for this PC

Best measured model for you: gemma4-26b-a4b-qat  (MoE with ~4B active params: fastest)
What it can do here:
  TH  real local school/licence exams   65.7% correct  <- your language
  holds ~78k tokens at once (~130 pages of text) next to the model
  uses ~34.7 GB of system RAM: ~0.3 GB embeddings/CPU-mapped + ~8.0 GB prompt cache + ~25.9 GB ctx checkpoints + ~0.5 GB host (est.)
  leaves ~0 GB of RAM free for other apps (est.)
  answers at ~85 tok/s

Speeds marked est. come from your card's memory bandwidth, calibrated on measured runs. Everything else is measured.

Less RAM than running llama.cpp yourself (0.2)

llama-server's defaults keep a prompt cache of up to 8 GiB plus 32 conversation checkpoints in system RAM, so RAM keeps growing while you chat. localllm sizes both to your PC. Same 30-turn chat, RX 9070 XT, 32 GB RAM:

About 4x less RAM, same speed, ~15% less CPU per answer, ~0% CPU while idle. On cards too small for gemma-4, its experts can stay in RAM: 45 / 36 / 31 tok/s with 8 / 13 / 18 layers' experts off the GPU (12 / 10 / 8 GB cards).

Smart routing between local models (experimental, opt-in)

localllm serve --models auto          # or --models qwen3.8-27b-q3,gemma4-26b-a4b-qat

Each message goes to the model with the best measured score for its language and task (math vs general). If all the models fit in VRAM together (24 GB+ cards for the pair below), they all stay loaded and routing is instant. Otherwise one model is on the GPU at a time and it only swaps when the other is at least 3 points better, because a swap costs seconds. Every answer says which model wrote it and why (X-Localllm-Model header; shown under each answer in localllm chat).

What we measured on an RX 9070 XT (16 GB) with 32 GB RAM, and why this is not the default:

Example: Chinese knowledge questions go to Qwen (+5.5), Chinese math goes to gemma (+4.4). On a 16 GB card a second model only pays off for Chinese and Spanish (Japanese +2.4 is inside the benchmark margin), and every swap costs 4-9 s. Keeping both models on the card by letting Windows page VRAM made both 4-5x slower, so that's not an option. Faster switching is tracked in #32: LoRA adapters on one shared base, or specialists small enough to sit next to the main model.

Measured results (RX 9070 XT 16 GB, Windows 11, llama.cpp Vulkan)

Accuracy (%) on multiple-choice exams, zero-shot. global = Global-MMLU-Lite: the same 400 questions translated, so languages compare like for like. regional = INCLUDE: real exams written in each country (ThaiExam for Thai).

Cells are global / regional. Margins are about ±4 (global) and ±5 (regional) points at 95%, so localllm treats gaps under 2 points as a tie and picks the faster model.

Findings worth knowing

1. 2-bit costs 8-13 points, and lower-resource languages pay the most. Hindi, Arabic and Thai lose 13; English, Chinese and Spanish about 8-9. A 177B MoE squeezed to 1.6 bits scored below a 27B at 3 bits.
2. Build 2-bit files from the original weights, and don't expect a small calibration set to beat a good vendor build. Re-quantizing an 8-bit file down to 2 bits cost Thai about 9 points (45.0 vs 54.2 for the same recipe made from BF16). A Thai or mixed-language importance matrix won most of that back (51-52 Thai) - but built from BF16, our mixed matrix was worse than Unsloth's own UD-IQ2_S in 3 of 4 languages by KL divergence (table below), so we are not publishing it. At ~3.5 bits the matrix made no difference (64.8 vs 64.6 Thai). Details in #24.
3. AMD/Intel cards without Resizable BAR: the Vulkan fix is model-dependent - so measure. GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1 makes Qwen3.8-27B (hybrid DeltaNet, rewrites a 3 MB state per layer per token) 1.64x faster on an RX 9070 XT with ReBAR off, but gemma-4-26B-A4B 7-9% slower. localllm applies it per model, and localllm tune measures it on your PC. Note: llama.cpp treats any value, even 0, as on - unset it to turn it off. See llama.cpp#27097.
4. Qwen3.8 GGUFs ship a multi-token-prediction head. Drafting 2 tokens with it adds ~40% decode speed for free; drafting 3 is slower.
5. The first run of a new llama.cpp build is slow while the GPU driver compiles its shaders once (~15 s).

How much each quant changes the model, per language

KL divergence from the Qwen3.8-27B Q8_0 reference (lower is better) and how often the most likely next token stays the same, on held-out Wikipedia text (8 x 512 tokens per language; tools/kld_per_language.py):

Going from 3 to 2 bits multiplies the KL divergence 4-6x in every language, and top-token agreement ends 2-9 points lower in Arabic, Thai and Hindi than in English - the same order as the benchmark losses.

How the benchmark works

localllm eval asks each question with thinking off and reads the log-probability of every answer letter from the first generated token, then takes the most likely one. It's prompt processing only, so a language takes a few minutes, and the result is deterministic. Data is downloaded at eval time from the original Apache-2.0 datasets (Global-MMLU-Lite, INCLUDE, ThaiExam) and never redistributed. It measures knowledge and reasoning in multiple choice, not writing quality.

Contributing

The catalog only grows with measurements. Run localllm eval --langs en,<yours> on your GPU and open a PR with ~/.localllm/results.json and your GPU name. Other languages' local exams are very welcome. See ROADMAP.md for what's next: using less system RAM (0.2), working alongside cloud provider APIs (0.3), a speed-only release (0.4), and per-language compression research (0.5).

License

MIT. Models keep their own licenses; benchmark data keeps its own (Apache-2.0).