Datasets:

localllmrig
/

localllmrig-open-data

weights_gb = params_b × bytes_per_param, with bytes per parameter Q4 (Q4_K_M) 0.60, Q5 0.71, Q8 1.07, FP16 2.0. Not measured GGUF file sizes. Reference point: a bartowski Llama-3.3-70B Q4_K_M file is 42.52 GB; 70.6 × 0.60 = 42.36 GB.
kv_gb = FP16 KV cache at 8,192 tokens: 2 × kv_heads × head_dim × 2 bytes × layers × tokens / 1e9, adjusted on the site for sliding-window layers (Gemma 2). Architecture values link to each model's config.json (architectureSourceUrl).
overhead_gb = 0.8 GB fixed.

2. Usable memory (assumption)

Discrete GPUs: card VRAM − 0.6 GB per GPU.
Apple unified memory: total − macOS reserve (6 GB at ≤16 GB, 7 at ≤24, 8 at ≤36, 10 at ≤64, 12 above).
Laptops whose GPU-addressable memory is not published: unscored. No fit or speed is given.

3. Fit

full: total need ≤ usable memory.
offload: discrete-GPU PC where the model fits in usable VRAM + (system RAM − 8 GB). Speed drops sharply.
no: does not fit.

4. Decode speed (estimate)

Decode is treated as memory-bandwidth-bound:

tok/s = 1 / (streamed_gb / effective_bw + 0.0002 s) with streamed_gb = weights_gb + kv_gb.

effective_bw = spec_bandwidth × utilization × multi_gpu_factor
Utilization assumptions: NVIDIA 0.78, AMD 0.40, Apple 0.48. Multi-GPU layer split: 0.55 × summed bandwidth.
Offload: GPU part at effective GPU bandwidth, the rest at system-RAM bandwidth × 0.45.

These are engineering assumptions, not benchmarks. Real speed depends on runtime, drivers, batch size, context fill, prompt length and quant format. Prompt processing (prefill) speed is not modelled.

5. Prices

EUR including VAT, one figure per hardware row, labelled by type: used (median of sold listings), new (lowest in-stock EU), new (out of stock), an Apple Store BE price, or an estimate. price_verified is true only when a source URL is stored. PC build totals are GPU + a RAM line + a €500 rest-of-system estimate and are not checked system prices. USD = EUR × the stored FX rate and is an estimate.

eur_per_gb_vram = price_eur_incl_vat / memory_gb for single cards with a price.

6. Showdown (measured, CPU only)

llama.cpp b11707 official CPU binary, llama-server OpenAI-compatible endpoint, chat template via --jinja.
Cloud VM: 8 vCPU Intel Xeon (AVX-512, AMX), 15 GB RAM, no GPU, shared with other workloads.
Q4_K_M GGUF per model (URL and sha256 in the data), temperature 0, seed 42, max 768 tokens, context 4,096, 8 threads, no system prompt, thinking off.
Speed = timings.predicted_per_second per prompt; median, min and max over 10 prompts.
One run per prompt, no retries, no cherry-picking. Outputs at the 768-token cap are flagged truncated_at_cap.
Greedy decoding on a multi-threaded CPU is not bit-for-bit reproducible: Llama 3.2 3B was run twice and 4 of 10 outputs were identical.
Only the 4 prompts with one right answer were checked by hand against reference answers; the check text is published verbatim. Writing, translation and summaries are not scored.

7. Reproducing

python3 scripts/build.py regenerates every file in data/ from snapshot/. python3 scripts/validate.py compares the fit/speed matrix with the live /api/recommend endpoint; on 17 Oct 2026, 168 picks matched on fit and agreed within 0.1 tok/s (the small gap comes from the API rounding KV size for sliding-window models).