DeepSeek V3 0324 - cheapest: DeepInfra $0.24/M input

DeepSeek V3 0324, a 671B-parameter Mixture-of-Experts model with 37B active per token and a 128k context window, is available on DeepInfra at $0.24 per million input tokens, the cheapest listed provider. The model achieves a general score of 87 and a value score of 363 points per dollar per million input tokens. DeepInfra's uptime is 52.25%, while SiliconFlow offers 98.89% uptime at $0.25 per million input tokens.

DeepSeek V3 0324 MoE workstation671B total, 37B active per token. Q4 K M requires ~360GB RAM/VRAM. Use Q2 K ~180GB for DGX Spark. - 671.0B - 128k - mit - Mar 2025 Scores Score per dollar 363 pts per $/M input general score 87 divided by cheapest input price $0.24/M . Higher is better value. See live pricing /models/deepseek-v3-0324/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 | Q2 K | Q4 K M | |---|---|---| | NVIDIA Jetson Orin NX 16GB | | no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing 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 | |---|---|---|---|---|---|---|---|---| | SiliconFlow | API | 0.25 | 1.00 | 0.135 | - | - | 98.89% | best uptime | | Novita | API | 0.27 | 1.12 | 0.135 | - | - | 97.79% | | | DeepInfra avoid | API | 0.24 | 0.90 | 0.135 | - | - | 52.25% | cheapest | 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/deepseek-v3-0324/pricing See who runs DeepSeek in production → /adoption/deepseek 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.