【実測】あなたのGPUで動く最強ローカルLLM 2026年7月版 — VRAM階級別ベンチマークA practical benchmark guide selecting the best local LLM per VRAM tier (6 GB…

Choosing a local large language model is increasingly a question of hardware rather than raw capability, and a new benchmark guide published on Zenn tackles that problem directly by matching specific models to specific VRAM budgets. For anyone running models on their own machine — whether to keep data private, cut API costs, or work offline — knowing which model actually fits and performs well within a given memory tier is often more useful than abstract leaderboard rankings.

The guide's central premise is a conclusion-first table that pairs each VRAM class with a recommended winner. At the entry level, a 6 GB card is matched with Qwen3.5-4B quantized to Q4_K_M, a 2.74 GB file that the author reports can address a context window of 262,144 tokens. Moving up to 8 GB, the recommendation shifts to Qwen3.5-9B at the same Q4_K_M quantization, weighing in at 5.68 GB, with what appears to be a similarly large context window. The Qwen3.5 family dominates these lower tiers in the author's assessment.

A notable strength of the piece is its transparency about where each number comes from. Every figure is tagged either as a direct measurement or as a cited value. Measured numbers were captured on the author's own test system, an Apple M5 Pro with 48 GB of unified memory, with the raw data published in full. Cited numbers are drawn from public primary sources and linked in place. Importantly, the author states the test machine has no NVIDIA GPU, so all NVIDIA-related figures in the article are cited rather than measured — a caveat worth keeping in mind when comparing across platforms.

That methodology highlights a broader point about how local inference is measured today. On Apple Silicon, the CPU and GPU share a single pool of unified memory, so the practical ceiling on model size is total RAM rather than a discrete VRAM figure. On a typical desktop with a dedicated NVIDIA card, the GPU's VRAM is the hard constraint, and spilling over into system RAM usually degrades speed sharply. Numbers gathered on one architecture do not translate cleanly to the other, which is why the measured-versus-cited distinction matters.

Quantization is the technique that makes these tier recommendations possible. Formats such as Q4_K_M compress a model's weights to roughly four bits each, drastically shrinking the file and memory footprint while retaining most of the model's quality. This is why a 4B or 9B parameter model can run comfortably on modest consumer hardware; the trade-off is a small, usually acceptable loss in precision compared with full 16-bit weights. The K_M designation refers to a specific mixed-precision scheme that many users regard as a good balance between size and fidelity.

The tooling context is equally relevant. The article is tagged for Ollama, one of the most popular runners for local models, which packages downloads, quantization variants, and a simple serving interface into a single command-line tool. Ollama and comparable projects such as llama.cpp and LM Studio have lowered the barrier to running capable models at home, and the GGUF file format they rely on is what the quantization labels above describe.

Context also comes from the models themselves. Alibaba's Qwen series has become a frequent fixture in local-LLM discussions because its open-weight releases span a wide range of parameter sizes and are competitive with other open families such as Meta's Llama and Google's Gemma. A 262,144-token context window, if borne out in practice, would be unusually large for models this small, and readers may want to verify how retrieval and speed hold up when that window is actually filled.

As with any single-author benchmark, the results should be read as a useful, well-documented starting point rather than a definitive ranking. Performance depends heavily on the specific prompt, the runner version, and the exact hardware, and the dated framing — a July 2026 snapshot — reflects how quickly the local-model landscape shifts. Still, the tier-by-tier structure and the clear labeling of measured versus cited data make it a practical reference for matching a model to the GPU you already own.