# DeepSeek V4 Flash 0731 > Source: > Published: 2026-08-03 14:44:43+00:00 # DeepSeek V4 Flash 0731 MoE workstation**284B total, 13B active per token (MoE).** Same FP4+FP8 hybrid-attention family as V4 Pro: Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) across 61 layers, manifold-constrained Hyper-Connections (mHC), Muon optimizer, 32T+ pretraining tokens. - **Context:** 1M native, 384K max output; three modes (non-think / think-high / think-max). **The 2026-07-31 iterative update of V4 Flash** (supersedes the April model). Per Artificial Analysis, a 10-point Intelligence Index jump to 50 - 6 points above V4 Pro, 1 behind GLM 5.2 / GPT-5.6 Luna, 7 behind Kimi K3. Agentic Elo 1559 (up from 1189), Terminal-Bench 2.1 79% (+17), Humanity’s Last Exam 37% (+5), GPQA-Diamond 91% (+1), SciCode 50% (+5). Token usage -12%; hallucination rate 84% (a 12-point drop); AA-Omniscience Index -16 (+7). Pricing unchanged at $0.14/$0.28 per 1M in/out (cache-hit $0.0028/M, a 98% discount). The model the `deepseek-chat` /`reasoner` API aliases now route to (retired 2026-07-24). - **Local run (2026-07-31):** Unsloth’s Dynamic 2.0 GGUFs landed local inference.**UD-Q4_K_XL** is a 155GB lossless 4-bit build (~168GB RAM);**UD-Q8_K_XL** is a 162GB 8-bit full-precision build (~175GB RAM, only 7GB bigger than Q4 because the 13B active experts dominate). Both fit two stacked DGX Sparks (256GB unified via ConnectX-7) or a 192GB+ unified rig; a smaller 3-bit (~110GB RAM) that would fit a single 128GB Spark is announced but not yet published. Run via Unsloth or`llama.cpp -hf` - Ollama only ships the`:cloud` endpoint, so there is no local Ollama tag. Open weights under MIT (full weights expected in the coming weeks per DeepSeek; the Unsloth GGUFs are available now). - 284.0B - 1000k - mit - Jul 2026 ## Scores ## Score per dollar 1000 pts per $/M input general_score (90) divided by cheapest input price ($0.09/M). Higher is better value. [See live pricing](/models/deepseek-v4-flash-0731/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 | UD-Q4_K_XL | UD-Q8_K_XL | |---|---|---| | 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)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 28 days ago via OpenRouter. Click a column to sort. some pricing may be stale - last verified 2026-07-07 | Provider | Type | Input $/M | Output $/M | Cache $/M | Tok/s | Latency | Uptime | Value | |---|---|---|---|---|---|---|---|---| | | API | 0.09 | 0.18 | - | - | - | - | 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-v4-flash-0731/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.