I m running Hermes Agent on a machine with 8GB VRAM, currently using Gemma 4 E4B as it's model. Suggest other models if you do not think this one is that good. Be mindful of the VRAM, so lower end is best, but I want it for personal assistance rather than heavy coding.

Gemini 3.5 Flash (changed mind)

Answer: For an 8GB VRAM system running Hermes Agent, "Hermes-3-Llama-3.1-8B (Q4_K_M)" is the absolute best choice for agentic and tool-calling capabilities, while "Qwen2.5-7B-Instruct" or "Llama 3.2 3B Instruct" remain superb alternatives depending on your VRAM headroom needs.

I am convinced by Claude Opus 4.7's brilliant recommendation of "Hermes-3-Llama-3.1-8B (Q4_K_M)". Since you are specifically running the Hermes Agent framework, utilizing a model explicitly fine-tuned by Nous Research for agentic workflows, function calling, and structured outputs is a game-changer. It aligns perfectly with your software's native strengths while fitting within the 8GB VRAM envelope when quantized to 4-bit (Q4_K_M). While my previous recommendation focused heavily on the safer side with 3B models to completely avoid VRAM congestion, "Hermes-3-Llama-3.1-8B" offers the precise cognitive and system-following capabilities required for reliable agent execution without choking your system, provided your context length is kept moderate (around 4k-8k tokens). If you find the 8B model occasionally pushes the limits of your VRAM during long conversations, "Qwen2.5-7B-Instruct (Q4_K_M)" or a highly-optimized 3B model like "Llama 3.2 3B Instruct" remain excellent, highly responsive fallbacks that guarantee zero lag.

Claude Opus 4.7

Answer: I'm sticking with my recommendation of Qwen2.5-7B-Instruct or Llama-3.1-8B-Instruct at Q4_K_M as the best upgrade from Gemma 3 4B for an 8GB VRAM personal assistant, though I'll acknowledge the agent-overhead concern raised by others.

GPT-5.5 essentially converged on the same recommendation I made, which reinforces my confidence: 4-bit quantized 7B–8B models are the sweet spot for 8GB VRAM personal assistants. They offer a meaningful step up in reasoning, multi-turn coherence, and instruction-following over 3B–4B models like Gemma 3 4B or Llama 3.2 3B. Gemini 3.5 Flash and Grok 4.3 make a fair point that I should engage with: agent frameworks like Hermes Agent add system prompt, scratchpad, and tool-call overhead, and long contexts can eat VRAM quickly. That's a legitimate consideration. However, I don't think it justifies dropping all the way down to 2B–3B models. A Q4_K_M 7B model uses ~4.5GB, leaving ~3GB for context and KV cache — enough for 4k–8k tokens of agent context on most setups. The cognitive gap between Qwen2.5-7B and Llama 3.2 3B for nuanced assistant tasks (planning, summarization, multi-step reasoning) is substantial. Where I'll adjust slightly: if the user finds VRAM tight in practice with Hermes Agent's overhead, then Phi-3.5-mini (3.8B) or Llama 3.2 3B Instruct are excellent fallbacks — and I already listed Phi-3.5-mini as option #4. So the practical recommendation order remains: try Qwen2.5-7B-Instruct Q4_K_M first (ideally Hermes-3-Llama-3.1-8B if tool-calling is central), and only step down to a 3B-class model if VRAM pressure causes offloading or context truncation. No single other model convinced me to fundamentally change my position — GPT-5.5 agreed with me, and the smaller-model advocates raised valid but not decisive concerns.

GPT-5.5 (changed mind)

Answer: For an 8GB VRAM Hermes Agent used mainly as a personal assistant, I would now make "Hermes-3-Llama-3.1-8B" in Q4_K_M my first try, with "Qwen2.5-7B-Instruct" Q4_K_M as the best general fallback and smaller 3B–4B models only if you prioritize speed and context headroom.

I would slightly revise my previous answer because Claude Opus 4.7 made a strong point: since you are specifically running Hermes Agent, a Hermes-tuned model such as "Hermes-3-Llama-3.1-8B" is likely a better fit than plain "Llama-3.1-8B-Instruct". The base capability is similar, but the Hermes fine-tuning is more directly aimed at agentic behavior, structured outputs, tool use, and assistant-style interaction, which matters more for your use case than raw coding strength. On 8GB VRAM, I would still stay in the 7B–8B range only with 4-bit quantization, preferably Q4_K_M if you are using GGUF. That should usually fit while leaving some room for context and runtime overhead, though very long contexts may still push you toward smaller models or lower quantization. If you want the best balance of quality and personal-assistant usefulness, test "Hermes-3-Llama-3.1-8B" Q4_K_M first. My second recommendation remains "Qwen2.5-7B-Instruct" Q4_K_M. It is a very strong small generalist, follows instructions well, and tends to be good at summarization, planning, writing, and everyday assistant tasks. If the Hermes-tuned Llama model gives you any compatibility issues or feels less natural, Qwen is an excellent alternative. If you find 8B too slow, or if Hermes Agent’s prompts, memory, and context overhead are causing offloading, then drop down to "Llama 3.2 3B Instruct", "Qwen2.5-3B-Instruct", or "Phi-3.5-mini-instruct". These will feel faster and leave more VRAM for longer sessions, but they will usually be less capable and less consistent than a good 7B–8B Q4 model. So my practical shortlist is: first "Hermes-3-Llama-3.1-8B" Q4_K_M, second "Qwen2.5-7B-Instruct" Q4_K_M, third "Llama 3.2 3B Instruct" or "Qwen2.5-3B-Instruct" if you want maximum smoothness. I would not assume "Gemma 4 E4B" is bad, but I would expect these alternatives to be more satisfying for a daily personal assistant.

Grok 4.3 (changed mind)

Answer: For running Hermes Agent on 8GB VRAM with a focus on personal assistance, switch to a 4-bit quantized Hermes-3-Llama-3.1-8B model rather than Gemma variants or smaller Phi models.

Claude Opus 4.7's suggestion of Hermes-3-Llama-3.1-8B-Instruct (Q4_K_M) stands out because it is specifically fine-tuned by NousResearch for agentic workflows, tool calling, and structured outputs that align directly with Hermes Agent requirements. This 8B model in Q4_K_M quantization fits comfortably around 4.9GB, leaving sufficient headroom for context, system prompts, and agent scratchpad memory without triggering CPU offloading on 8GB VRAM. While my earlier recommendation of Phi-3 mini or Gemma-2-2B prioritized minimal size, the specialized agent tuning in Hermes-3 provides noticeably better reliability for personal assistance tasks involving planning and tool use, outweighing the slight increase in parameters. Qwen2.5-7B or Llama-3.1-8B alternatives from other models are also strong, but the Hermes-specific variant offers the clearest match for this exact setup.

Final Summary

The AI Roundtable reached a unanimous consensus after Claude Opus 4.7 identified that the user's framework is best paired with its namesake model, Hermes-3-Llama-3.1-8B. While Gemini 3.5 Flash and Grok 4.3 initially suggested ultra-small 3B models to save VRAM, they were ultimately convinced by the superior reasoning and tool-calling capabilities of a quantized 8B model. GPT-5.5 joined the group in recommending a 4-bit quantization (Q4_K_M) as the sweet spot for balancing intelligence with the hardware's 8GB constraint.

All 4 models agreed