WhichLLM: Compare local model performance

The guide whichllm is your technical resource for navigating the complex ecosystem of open-weight models that can run locally on Mac or PC. As these architectures proliferate, knowing which model to choose based on your hardware constraints and specific needs becomes crucial. This article provides an in-depth comparative analysis of the technical specifications, hardware requirements (VRAM), and real-world performance of the models indexed on quelllm.fr. We will break down the key criteria for optimizing your local deployment based solely on our reference catalog Model Catalog.

⚙️ Selection criteria: VRAM, Size, and License

Before evaluating reasoning or code-generation quality, it is essential to understand the hardware constraints. Model size (parameter count) directly determines the memory footprint required for efficient Q4 quantized execution.

Required VRAM (Q4 quantization) : This is the main limiting factor on a local workstation. A larger model requires proportionally more VRAM. For example, compare DeepSeek V4 Pro 1.6T (about 960 GB in Q4) with models such as Llama 3.1 405B Instruct (approximately 240 GB in Q4) shows a significant scale difference for deployment on consumer GPUs or high-end workstations. To refine this comparison, consult the technical specifications for DeepSeek V4 Pro 1.6T.

Examples of hardware requirements based on our catalog : * For models in the 100B to 200B range (e.g.: Mixtral 8x22B Instruct, Command R+ 104B (08-2024)), a graphics card with at least 16 GB of VRAM is often necessary for comfortable use. * Very large models, such as DeepSeek V4 Pro 1.6T or MiMo V2.5 Pro, require professional multi-GPU configurations (several hundred GB of VRAM). For a precise estimate, use our interactive configurator.

Licenses and Usage : The license is a non-negotiable aspect of any professional use. We clearly distinguish models under Apache 2.0 (very permissive) like Qwen 3.5 122B-A10B, from MIT which offers similar flexibility, or specific licenses such as the DeepSeek License pour DeepSeek V3 671B. You should consult the details at https://quelllm.fr/guide/licences before any deployment, especially if you anticipate intensive commercial use.

🧠 Performance and Capabilities: Benchmarks and Context Window

Performance isn’t just about parameter count. Two key metrics are context window (context window) and scores on specific benchmarks.

Context Window ($\text{ctx}$) : A model’s ability to “remember” a long conversation or an entire document is measured by its context window. Some models excel in this area, such as Inkling with a $\text{ctx}$ of 1048576 tokens, enabling analysis of very large corpora. Conversely, older models or those optimized for speed may have a limited context window (e.g.: Grok-1 (base) with only 8192). To compare context capabilities, look at Inkling.

Specific Benchmarks and Reasoning Tasks : Although scores vary depending on the evaluation framework used HuggingFace Leaderboard, we observe clear trends. For programming, Kimi K2.7 Code (1059B) is a relevant candidate to test locally. For general reasoning tasks, compare GLM 5.2 753B-A40B contre Mistral Large 3 675B can help guide the choice based on the complexity of the problem you submit to the LLM GLM vs. Mistral comparison.

🚀 Inference Speed (Tokens/sec) and Local Optimization

Speed, measured in tokens per second ($\text{tokens}/\text{sec}$), is inherently tied to the amount of GPU resources allocated and the selected quantization level. A highly capable but slow model may be unusable in a real-time application.

Local optimization often comes down to choosing the format (GGUF, AWQ) and precision level. On our platform, we list Q4 specifications to provide a consistent basis for comparison. For example, DeepSeek V4 Flash 284B offers an excellent balance between size and the ability to handle long contexts (1000000 tokens), which is crucial for processing continuous streams without information loss [DeepSeek V4 Flash].

For a more thorough speed evaluation, see our guide/optimisation-inference to understand how different techniques affect actual $\text{tokens}/\text{sec}$ on your hardware. We also have specific comparisons, such as the one between MiMo V2 Flash et DeepSeek V4 Flash 284B.

🛠️ Practical Use Cases by Model

The model choice should be dictated by the task:

• Advanced Code Generation : Specialized models such as Kimi K2.7 Code or fine-tuned versions of DeepSeek V4 Pro 1.6T should be prioritized, while checking their license for commercial use GitHub LLM Repositories.
• Massive Document Analysis (RAG) : Models with a large context window, such as Inkling or Llama 4 Maverick 400B are ideal for ingesting and querying large databases without manual chunking. For RAG use, test MiniMax M3.
• High-Level Conversational Chat : Proven architectures such as Mistral Medium 3.5 128B or Qwen 2.5 72B Instruct offer a good balance between conversational quality and reasonable hardware requirements for everyday use on a PC.

❓ FAQ on local LLM deployment

Q: What is the main difference between Q4 and FP16 models?

Quantization (Q4) reduces the model's numerical precision, drastically lowering the VRAM footprint required compared with FP16. This makes it possible to run much larger models on consumer graphics cards, with minimal perceived loss in output quality Quantization Techniques Paper. The trade-off is always evaluated against the specific task.

Q: How do you choose between a 7B model and a 70B model?

The choice depends on the performance/resources trade-off. A small model will be fast on modest GPUs, while a large model will offer better consistency and reasoning depth but will require significantly more VRAM to maintain the quality expected in benchmarks [7B vs. 70B comparison].

Q: Are models with very long contexts always better?

No. A large context window is useful if your task requires analyzing an entire document (e.g.: Inkling). However, if the task is simple classification or short-form generation, the oversized context provides no benefit and unnecessarily increases operational costs in compute time.

Q: Which model is most accessible for getting started locally?

To get started without investing in expensive workstations, I recommend exploring models in the 30B to 50B parameter range, such as MiMo V2 Flash or Step 3.5 Flash. These models deliver good quality while remaining manageable on standard consumer-grade configurations [best-llm/beginner].

Q: How do you assess a model's suitability for code?

For code generation and correction tasks, it is crucial to prioritize models trained specifically for this purpose. Kimi K2.7 Code or specific versions such as Qwen3-Coder-Next 80B-A3B are good starting points to test locally.

Conclusion: Your Guide to Choosing with WhichLLM

L'outil whichllm provides the framework needed to turn raw information from open-weight LLMs into informed technical decisions. By cross-referencing VRAM requirements, available context, and the license, you can determine whether a model like DeepSeek R1 671B or Llama 4 Scout 109B matches your current infrastructure. For a dynamic comparison based on your actual hardware specifications (VRAM/GPU), use our interactive configurator.

Author: Mohamed Meguedmi