Mistral Nemo 12B - cheapest: DekaLLM $0.02/M input

DekaLLM offers the cheapest inference for Mistral Nemo 12B at $0.02 per million input tokens, delivering 3,444 points per dollar based on a general score of 62. The 12.2-billion-parameter model supports a 128k context window and is released under Apache 2.0 license as of July 2024.

Mistral Nemo 12B consumer- 12.2B - 128k - apache 2.0 - Jul 2024 Scores Score per dollar 3444 pts per $/M input general score 62 divided by cheapest input price $0.02/M . Higher is better value. See live pricing /models/mistral-nemo-12b/pricing . Run it locally Per-quant memory needs and a static "can you run it?" reference - no rig entry required Can you run it? - reference rigs | Rig | Q4 K M | Q8 0 | |---|---|---| | 4x H100 80GB 320GB | fast 1009.4t/s | fast 558.3t/s | | 4x RTX 5090 128GB | fast 539.9t/s | fast 298.6t/s | | 4x RTX 4090 96GB | fast 303.7t/s | fast 168.0t/s | | 2x RTX 5090 64GB | fast 270.0t/s | fast 149.3t/s | | 2x RTX 3090 48GB | fast 141.0t/s | fast 78.0t/s | | Single RTX 5090 32GB | fast 135.0t/s | fast 74.7t/s | | Mac Studio M4 Ultra 192GB | fast 89.7t/s | fast 49.6t/s | | Mac Studio M4 Ultra 512GB | fast 89.7t/s | fast 49.6t/s | | Single RTX 4090 24GB | fast 75.9t/s | fast 42.0t/s | | MacBook Pro M5 Max 128GB | fast 50.5t/s | fast 27.9t/s | | Single GTX 1080 Ti 11GB | fast 36.5t/s | offload | | DGX Spark 128GB unified | fast 20.6t/s | ok 11.4t/s | | Epyc + 512GB DDR4-3200 + 2x RTX 3090 | ok 15.4t/s | ok 8.5t/s | | Epyc + 512GB DDR4-2400 + 2x RTX 3090 | ok 11.6t/s | slow 6.4t/s | | NVIDIA Jetson Orin NX 16GB | slow 7.7t/s | slow 4.3t/s | Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast =20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark. How can a 24GB GPU run a 744B model? It does not load the model into VRAM. The quantized weights e.g. ~410GB at Q4 sit in system RAM; the GPU only holds the small shared attention and router tensors and accelerates prompt processing. Because GLM 5.2 is a Mixture-of-Experts model, each token activates only ~40B of its 744B params, so llama.cpp streams just those active experts from system RAM to the GPU each token the -cmoe offload path . That makes decode speed bound by system-RAM bandwidth, not GPU bandwidth - single digits on DDR4, which is why these rigs show 3-8 t/s even though they “fit.” A bigger GPU e.g. 2x 3090 keeps more experts resident on-card and raises tok/s; a smaller GPU still runs it but pays the bandwidth tax. A 744B dense model could not run this way - only MoE’s small-active-params trick makes it possible. Aggressive quants 1-2 bit trade accuracy for size - roughly 17% accuracy loss at 2-bit vs full precision, and real long-context work often needs Q5 or Q6 even when lower quants “fit.” Formula estimates here are conservative; real tuned setups can exceed them one HN user reports ~6 tok/s on a 512GB DDR4 + 2x 3090 rig . Download options Or run it in the cloud Live per-provider pricing, throughput and uptime - refreshed about 1 hour ago via OpenRouter. Click a column to sort. | Provider | Type | Input $/M | Output $/M | Cache $/M | Tok/s | Latency | Uptime | Value | |---|---|---|---|---|---|---|---|---| | DeepInfra | API | 0.02 | 0.03 | - | - | - | 99.95% | best uptime | | Mistral | API | 0.15 | 0.15 | 0.015 | - | - | 99.94% | | | DekaLLM | API | 0.02 | 0.03 | - | - | - | 98.03% | cheapest | | Novita avoid | API | 0.04 | 0.17 | - | - | - | 69.74% | Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing. Detailed API pricing page + JSON endpoint → /models/mistral-nemo-12b/pricing See who runs Mistral AI in production → /adoption/mistral Inference cost over time Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.