Verified result
Performance class
This run ranks #6 among 53 verified runs on VRAM Check.
Ranks compare published benchmark runs, not unique GPU models. The same GPU model may appear more than once. Comparison for TinyLlama 1.1B (Q4_K_M) / Q4_K_M, not overall AI performance.
Tasks to explore: Coding · General chat · Math / reasoning · Embeddings.
User-prepared RTX 5070 Ti illustration. Board design and framing may differ.
NVIDIA GeForce RTX 5070 Ti9.4 tok/s separates this run from #5 globally.
See the sections below for model suggestions and possible next steps.
The computer completed the standard test, passed every required check, and earned a public ranking.
This is the verified text generation speed used to rank this computer.
These measurements show how quickly the model reads, starts answering, and continues writing, plus how consistent the test was.
Choose a task, compare memory requirements, and open a model to explore compatible hardware.
79 of 139 catalog profiles meet the memory estimate.
Three points across the memory range, not a model-quality ranking.
Within the planning budget.
Memory fit is a starting point, not a speed guarantee. Context length, runtime and other applications can change actual memory use. Only the benchmark model was timed.
Compatibility estimates updated Oct 19, 2026. They combine this measured run with the model catalog; each suggested model was not benchmarked directly.
Good starting points: Codestral 22B · Granite Code 20B · GPT-OSS 20B.
The clearest next unlocks are Kimi Linear 48B-A3B Instruct · Gemma 4 31B Instruct · Gemma 4 26B-A4B Instruct.
Good starting points: GPT-OSS 20B · Gemma 4 E4B Instruct · Phi-4.
The clearest next unlocks are Kimi Linear 48B-A3B Instruct · Mixtral 8x7B Instruct · Falcon 40B Instruct.
Good starting points: GPT-OSS 20B · Gemma 4 E4B Instruct · Gemma 4 12B Instruct.
The clearest next unlocks are Kimi Linear 48B-A3B Instruct · Gemma 4 31B Instruct · Gemma 4 26B-A4B Instruct.
Good starting points: Qwen3 Embedding 8B · mxbai-embed-large · Nomic Embed Text v1.5.
More capacity would make this task feel safer, but it would not unlock a new class of models yet.
See the main constraint, the evidence behind it, and why the recommendation focuses there.
For the model tested here, memory and speed are well balanced. An upgrade only makes sense if you want to run larger models or get answers faster.
For this test, there is no single weak point dominating the result.
The current computer is well balanced for the tasks it can run today.
The next step is about chasing a more ambitious target, not repairing a problem in the current build.
An upgrade is optional rather than urgent. Change hardware only if you want to run larger models or get answers faster.
Start with a model, then compare the hardware it needs.
Choose the model you want to run. Compare hardware that meets its estimated memory needs, not a generic upgrade tier.
Your starting point: 14.4 GB model budget / 63.9 GB system RAMMemory estimates use the selected quantization and the planner's reserved-memory budget. Context size, runtime support and other applications change actual requirements. This is a shortlist for investigation, not a measured benchmark on the target model or a best-value award.
A desktop-style GPU name does not prove this computer is upgradeable. Check chassis clearance, power supply and connectors, motherboard support, and support for the chosen model on your operating system and inference runtime. Catalog prices can be stale.
The report above gives you the answer first. Open this area only when you want to compare individual models or inspect the repeated measurements behind the score.
Run the same benchmark and get a report grounded in your own hardware, workload conditions, and repeatable local inference.
