# GLM 5.2 - cheapest: Inceptron $0.90/M input > Source: > Published: 2026-07-09 14:12:02+00:00 # GLM 5.2 MoE workstation744B total, ~40B active per token (MoE: 256 routed experts, 8 active + 1 shared). Uses MLA + DeepSeek Sparse Attention (IndexShare) for a solid 1M context. Q4_K_M (~410GB) fits a 512GB Mac Studio M4 Ultra or 4x DGX Spark; Q2_K (~240GB) fits 2-3 DGX Sparks. Strongest open-source coding/reasoning model as of June 2026. - 744.0B - 1000k - mit - Jun 2026 1 person run this as their daily driver. [See the leaderboard](/daily-drivers) ## Scores ## 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 | Q3_K_M | Q4_K_M | Q5_K_M | BF16 | |---|---|---|---|---|---| | 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)[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)[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 10 hours ago via OpenRouter. Click a column to sort. | Provider | Type | Input $/M | Output $/M | Cache $/M | Tok/s | Latency | Uptime | Value | |---|---|---|---|---|---|---|---|---| | WandB | API | 1.39 | 4.40 | 0.260 | - | - | 100.00% | best uptime | | API | 1.40 | 4.40 | - | - | - | - | || | API | 3.00 | 10.25 | 0.500 | - | - | 100.00% | || | Alibaba | API | 1.32 | 4.14 | 0.264 | - | - | 99.97% | | | Novita | API | 0.98 | 3.08 | 0.182 | - | - | 99.95% | | | Fireworks | API | 2.10 | 6.60 | 0.210 | - | - | 99.94% | | | Together | API | 1.40 | 4.40 | 0.260 | - | - | 99.88% | | | StreamLake | API | 1.12 | 3.52 | 0.208 | - | - | 99.87% | | | AtlasCloud | API | 1.26 | 3.96 | 0.234 | - | - | 99.84% | | | API | 1.40 | 4.40 | 0.260 | - | - | 99.78% | || | Baidu | API | 0.97 | 3.07 | 0.181 | - | - | 99.73% | | | AkashML | API | 1.30 | 4.40 | 0.180 | - | - | 99.72% | | | Decart | API | 1.20 | 4.20 | 0.200 | - | - | 99.71% | | | SiliconFlow | API | 1.30 | 4.09 | 0.260 | - | - | 99.64% | | | Ambient | API | 1.40 | 4.40 | 0.260 | - | - | 99.51% | | | Z.AI | API | 1.40 | 4.40 | 0.260 | - | - | 99.47% | | | Io Net | API | 1.60 | 4.99 | 0.799 | - | - | 99.25% | | | DeepInfra | API | 0.93 | 3.00 | 0.180 | - | - | 98.84% | | | Parasail | API | 1.40 | 4.40 | 0.260 | - | - | 98.81% | | | Venice | API | 1.40 | 4.40 | 0.260 | - | - | 98.73% | | | Cloudflare | API | 1.40 | 4.40 | 0.260 | - | - | 98.47% | | | Inceptron | API | 0.90 | 3.08 | 0.180 | - | - | 98.04% | cheapest | | DigitalOcean | API | 1.05 | 4.40 | 0.210 | - | - | 97.56% | | | API | 0.98 | 3.08 | 0.182 | - | - | 97.15% | || | API | 1.40 | 4.40 | 0.700 | - | - | 95.48% | || | Morph | API | 1.10 | 4.10 | 0.180 | - | - | 95.32% | | | DekaLLM risky | API | 0.94 | 3.00 | 0.180 | - | - | 92.01% | | | Sub | - | - | - | - | - | - | $10.00/mo Coding Plan Lite | | | Sub | - | - | - | - | - | - | $20.00/mo Pro | | | Sub | - | - | - | - | - | - | $30.00/mo Coding Plan Pro | | | Sub | - | - | - | - | - | - | $80.00/mo Coding Plan Max | | | Sub | - | - | - | - | - | - | $100.00/mo Max | 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/glm-5-2/pricing) ## 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.