Every figure, size, date and benchmark score this video puts on screen, traced to the place that published it.
Weight file sizes are the Q4_K_M build unless stated otherwise, because that is the
quantisation a runner offers first and the one a viewer will actually download.
Released 4 August 2026.
| Claim | Value | Source |
|---|---|---|
| Q4_K_M weight file | 1.67 GB | LiquidAI/LFM2.5-2.6B-GGUF |
| Q4_0 weight file | 1.59 GB | same |
| Throughput, AMD Ryzen AI Max+ 395 | 113 tok/s | Liquid AI launch post |
| Throughput, Apple M5 Max | 220 tok/s | same |
The model card gives both figures but names only “an AMD Ryzen CPU”. The specific part, Ryzen AI Max+ 395, appears in Liquid AI’s own launch post, which is what the beat credits. | Context window | 128K | same |
Recommended uses, from the model card: “We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows.”
The limit the video quotes, also from the model card: “It is not recommended for agentic coding and knowledge-heavy tasks.”
Both throughput figures are the publisher’s own, measured on the machines named. Neither describes a GTX 1650.
Sources:
| Claim | Value | Source |
|---|---|---|
| Parameters | 3.8B | microsoft/Phi-4-mini-instruct |
| Context window | 128K | same |
| Q4_K_M weight file | 2.49 GB | bartowski/microsoft_Phi-4-mini-instruct-GGUF |
Microsoft’s own summary of what the model is for names “Strong reasoning (especially math and logic)”, which is the emphasis the video refers to.
Sources:
| Claim | Value | Source |
|---|---|---|
| LiveCodeBench v6 | 76.9 | Nanbeige/Nanbeige4.1-3B |
| Qwen3-30B-A3B-2507, same table, same benchmark | 66.0 | same |
| Community q4, Q4_0 | 2.33 GB | mradermacher/Nanbeige4.1-3B-GGUF |
| Community q4, Q4_K_S | 2.34 GB | same |
| Community q4, IQ4_XS | 2.24 GB | same |
| Community q4, Q4_K_M (the default) | 2.44 GB | same |
There is no official Nanbeige GGUF; every q4 above is a community build. The script says “around 2.3”, which is true of Q4_0, Q4_K_S and IQ4_XS but NOT of Q4_K_M, the build most runners pull by default. The picture therefore names the quant it is showing rather than printing a bare “2.3 GB”, because an unlabelled figure would read as the default one.
The comparison the video makes is the publisher’s own table: the 3B model’s 76.9 sits above Qwen3-30B-A3B-2507’s 66.0, a model with thirty billion total parameters. The rest of that column, for context: Qwen3-4B-2507 57.4, Qwen3-8B 49.4, Qwen3-14B 55.9, Qwen3-32B 55.7.
There is no official GGUF release, so the weight file sizes are community conversions: IQ4_XS 2.3 GB, Q4_K_S 2.4 GB, Q4_K_M 2.5 GB.
Sources:
Released 25 August 2026, Apache 2.0.
| Claim | Value | Source |
|---|---|---|
| Q4_K_M weight file | 2.24 GB | ibm-granite/granite-4.2-3b-GGUF |
| LiveCodeBench v6 | 69.71 | ibm-granite/granite-4.2-3b |
| Context window | 128K | same |
The three thinking modes the video describes are settings on the chat template:
enable_thinking=Trueenable_thinking=True, low_effort=Trueenable_thinking=FalseThe blank entries the video points at are real. On IBM’s own benchmark table the 3B model has no published result for SWE-bench Verified, SWE-bench Multilingual, SWE-bench Pro, Terminal-Bench 2.1, BirdBench or GDPval. The larger models in the same family do carry results in those rows.
Sources:
| Claim | Value | Source |
|---|---|---|
| What the E stands for | effective parameters | google/gemma-4-E2B-it |
| Parameters, with embeddings | 5.1B | same |
| Effective parameters, without | 2.3B | same |
| LiveCodeBench v6 | 44.0 | same |
| Q4_K_M weight file | 3.11 GB | unsloth/gemma-4-E2B-it-GGUF |
| Vision projector, F16 | 0.99 GB | same |
| Modalities | text, image, audio | google/gemma-4-E2B-it |
The gap the video is pointing at is between the name and the download. The name counts 2.3B effective parameters; the file has to store 5.1B, because per layer embedding tables are looked up rather than multiplied through, and they are still bytes on disk. The vision projector is a separate file on top of the 3.11 GB.
Sources:
| Claim | Value | Source |
|---|---|---|
| Parameters | 4B | Qwen/Qwen3.5-4B |
| LiveCodeBench v6 | 55.8 | same |
| Q4_K_M weight file | 2.74 GB | unsloth/Qwen3.5-4B-GGUF |
| Vision projector, F16 | 672 MB | same |
| Image input | yes | Qwen/Qwen3.5-4B |
Thinking can be turned off through the chat template: "enable_thinking": False passed in
chat_template_kwargs. Qwen notes that Qwen3.5 does not support the older soft switch.
Sources:
LiveCodeBench evaluates a model by running its generated solutions against test cases for programming problems, rather than comparing them to a reference answer as text. Version 6 is the contamination controlled window used by every score above.
The six scores this video shows, all version 6, all from the publisher of the model in question:
| Model | LiveCodeBench v6 |
|---|---|
| Nanbeige 4.1 3B | 76.9 |
| Granite 4.2 3B | 69.71 |
| Qwen3-30B-A3B-2507 | 66.0 |
| Qwen3.5 4B | 55.8 |
| Gemma 4 E2B | 44.0 |
They come from different labs running their own evaluations with their own settings, and they measure the full precision weights, not the four bit files that fit on a 4 GB card.
Source: https://livecodebench.github.io/
| Card | Memory | Bandwidth | Source |
|---|---|---|---|
| GTX 1650, GDDR5 | 4 GB, 128 bit | 128 GB/s | NVIDIA GTX 16 series |
| GTX 1050 Ti | 4 GB, 128 bit | 112 GB/s | PC Gamer, GTX 1650 review |
| RTX 3050 Laptop, 4 GB | 4 GB GDDR6, 128 bit | 192 GB/s | Notebookcheck |
The RTX 3050 laptop part is the newer card and moves memory half again as fast as a GTX 1650, and it still has the same four gigabytes to fit a model into. Bandwidth describes how fast memory moves data; capacity decides what loads at all.
Sources:
ollama ps reports placement in its PROCESSOR column: 100% GPU when the whole model is
on the card, 100% CPU when no usable GPU was found, and a split such as 48%/52%
CPU/GPU when the weights and the KV cache together are larger than the free VRAM. A
split is the runner doing its job, not a fault, and it is the first thing to read before
blaming the model for being slow.
Sources:
Every figure this video puts on screen was re-checked against a primary source after the script’s final rewrite. Confirmed exactly as shown: Liquid’s 1.67 GB Q4_K_M and the sentence ruling out agentic coding; phi 4 mini’s 3.8B and 2.49 GB; Nanbeige’s 76.9 against Qwen3-30B-A3B-2507’s 66.0 in its own table; Granite 4.2 3B’s August 25 release, 2.24 GB official Q4_K_M and 69.71; Gemma 4 E2B’s effective-parameter naming, 5.1B with embeddings, 44.0 and Unsloth’s 3.11 GB; Qwen3.5 4B’s 55.8, 2.74 GB, image input and disableable thinking; the GTX 1650 GDDR5 at 128 GB/s and the GTX 1050 Ti at 112 GB/s, both from NVIDIA’s own spec tables.
Three things were corrected rather than confirmed:
ollama ps listing on screen carried invented SIZE values. The column was removed;
the PROCESSOR column, which is the point of that beat, is documented behaviour.