Nodegrove VRAM: can I run it?
Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.
| Endpoint | https://mcp.nodegrove.io/mcp (streamable-http) |
|---|---|
| Registry version | 1.1.5 |
| Now | 6 tools · grade C (76) · pin ts_c326396d9e4d400314eee68f0c1ab14f · protocol 2025-06-18 |
| First / last listed | 2026-11-12 01:12 UTC / 2026-11-12 01:12 UTC |
| Grade over time | 2026-11-12: C (76), 6 tools |
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Tools: can_i_run what_fits estimate_vram estimate_from_hf_repo list_models list_gpus
can_i_run | Can this GPU run this open-weight LLM? Returns fits, tight or no, the memory split (weights, KV cache, overhead), a decode-speed ceiling, the longest context that fits and, on a no, every change that would make it fit: quantisation, KV cache, context, another card or a smaller model. Model: a name or id from list_models, any Hugging Face repo id, or its architecture. GPU: a name or id from list_gp params: model, architecture, active_params_b, gpu, vram_gb, bandwidth_gb_s, apple_silicon, quant, context, kv_cache |
what_fits | Which open-weight LLMs fit this GPU: every model in list_models checked at one quantisation and context, with a recommended everyday model (the biggest class that fits with room for context at conversational speed), the largest that fits, the best at Q8 and the first out of reach. GPU: a name or id from list_gpus, or vram_gb for any other card. params: gpu, vram_gb, bandwidth_gb_s, apple_silicon, quant, context |
estimate_vram | How much memory an LLM needs: weights + KV cache + overhead at each quantisation (or one), at a given context, and the smallest common card class that holds each. Model: a name or id from list_models, any Hugging Face repo id, or its architecture (params_b, layers, kv_heads, head_dim). params: model, architecture, active_params_b, quant, context, kv_cache |
estimate_from_hf_repo | Reads any Hugging Face model repo's config.json and parameter count and estimates its memory: the attention layout found (standard, sliding-window, hybrid or latent), how much each 1,000 tokens of context costs, and weights + KV cache + overhead at every quantisation. For models nodegrove.io has not reviewed; anything the reader cannot model is listed in warnings. params: repo*, active_params_b, context, kv_cache |
list_models | The open-weight LLMs nodegrove.io has verified against their config.json (data version 2026-11-03): id, size, attention design, native context, licence, memory at Q4 with 8k context and each model's page. params: search |
list_gpus | The GPUs and machines nodegrove.io covers: memory, the memory a runtime can use and bandwidth, from the makers' specs, with each one's page. params: search |
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