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Local models / hardware fit / 17 Oct 2026

The token stream is not the receipt.

A local-model speed preview can show whether a setup will feel brisk or painful. Buy hardware on a matched run that records the model, engine, prompt, memory route, and task clock.

Use the simulator to narrow the field. Use your workload to sign the purchase order.

A good preview admits what it predicts

Llmspeed lets a reader choose hardware, a model, and a quantization. It then simulates the wait before the first token and streams sample text at the predicted decode rate. The site labels each result as measured, derived, or projected. It also states that drivers, engine versions, flash attention, power limits, and thermals can move real results by 10 to 30 percent.

The public repository makes the mechanism inspectable. At revision 671c918, the calculator reads package size, estimated key-value cache, usable memory, backend coefficients, context depth, and active weight bytes. It uses separate paths for exact catalog combinations, formula-based projections, and partial offload. Those constants explain the preview. They do not turn the preview into a measurement on your machine.

Prediction quality is one question. Workload fit is another.

Measured still needs a test shape

Llmspeed asks benchmark contributors to run llama-bench -m your-model.gguf -p 512 -n 128. A submission records the hardware, model, quantization, backend, engine version, generation rate, prompt-processing rate, prompt size, source URL, and unusual settings.

The llama.cpp documentation keeps prompt processing and text generation separate. It repeats each test, reports an average and standard deviation, and can place the test at a chosen context depth. It also says the timer excludes tokenization and sampling. A measured decode rate can be valid while omitting startup, first response, tool calls, file reads, and retries.

A new model card does not fill the hardware row

DeepSeek published DeepSeek-V4.1-Flash-1017 today. Its model card lists 763 billion parameters, a one-million-token context window, and twelve linked quantizations. It reports publisher benchmark gains across coding and agent tasks. The card does not provide a consumer-machine throughput result for your chosen quantization and engine.

The release is a useful example because three questions arrive together. Can a package fit? Can the runtime serve it at an acceptable rate? Does it finish your coding work with fewer wrong turns? A model benchmark answers the third question only inside its named harness. A speed preview addresses parts of the first two. Neither replaces a matched local run.

Time the job, not only the decoder

Record cold load, first response, prompt processing, text generation, and total task time. Keep the prompt and generated token counts beside those clocks. Then replay a small coding workload with the same tools, repository, context policy, and stop rules you plan to use.

If the model spills weights into system memory, record that route. If a runtime update changes the result, record both revisions. If the fast setup needs another attempt after a bad edit, keep the retry in the task clock. Tokens per second describe one moving part.

Use the preview as a filter

  1. Choose two or three setups that appear to fit the model and budget.
  2. Pin one model artifact, quantization, and engine revision.
  3. Run prompt-processing and generation tests at a realistic context depth.
  4. Replay the same held-out coding tasks on every setup.
  5. Compare total task time, memory pressure, wrong actions, and recovery.
Interactive makeover / claim intake

Local speed claim dyno

Replace one tokens-per-second badge with a claim source, four test-shape records, and a copyable run packet. The dial shows packet completeness. It does not score model speed or truth.

Set the claim source

Choose the source behind the number. Then select the records your planned road test will capture.

Claim source
Road-test records
Run packet openMeasured source
Packet structure0 of 4 recordsThe claim has a source label. The local road test has no selected records.

Four road-test records are still open.

A source label does not supply a matched workload result.

What this component does. It records the claim type and drafts a matched-run packet. Selecting every record means the packet structure is ready. It does not run inference, validate a speed range, judge output quality, or approve a hardware purchase.

Sources read / 17 Oct 2026

Evidence bay

Product and source

Llmspeed

The live site supplies the interactive preview and its current catalog. The MIT-licensed repository explains the confidence labels, benchmark intake, prediction model, limits, and checked-in calculator. Source inspected at revision 671c9186ed975e6c41f326f364cd447ed6f9a476.

Open the preview / Inspect the revision
Benchmark contract

llama.cpp llama-bench

The documentation defines prompt processing, text generation, combined tests, repetitions, standard deviation, context depth, output formats, and timing exclusions.

Read the benchmark documentation
Model publisher

DeepSeek-V4.1-Flash-1017

The model card supplies the release scope, publisher benchmark table, prompt encoding notes, parameter count, context recommendation, license, and quantization links. It does not verify consumer hardware throughput.

Read the model card
Discovery route

Hacker News item 50127979

The official item ID resolves the Llmspeed launch and discussion. The discussion was used for discovery, not as proof of performance.

Open the discussion

Evidence boundary. Llmspeed documents its preview and model. llama.cpp documents its benchmark tool. DeepSeek documents its model and publisher results. Pimp My IDE designed the road-test packet. This dispatch did not download weights, install an inference engine, reproduce a throughput number, or test model quality.