A person is 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 running Hermes Agent on 8GB VRAM, the single best model is Hermes-3-Llama-3.1-8B (Q4_K_M), which is specifically optimized for agentic workflows and fits comfortably within your hardware constraints.

I am updating my recommendation after considering a brilliant connection pointed out by Claude Opus 4.7. Since you are explicitly running the Hermes Agent framework, utilizing a model designed by the same creators specifically for agentic workflows is the logical choice. Hermes-3-Llama-3.1-8B (quantized to Q4_K_M) is a state-of-the-art fine-tune of Llama 3.1. It is purpose-built by Nous Research for advanced agentic capabilities, offering superior function-calling, structured JSON outputs, and multi-step planning. By switching to this model, you get a seamless integration with your agent framework while maintaining the natural, polite conversational tone of Llama 3.1 for your personal assistant needs. From a resource perspective, the Q4_K_M quantization of Hermes-3-Llama-3.1-8B sits at roughly 4.8 GB. This leaves more than 3 GB of VRAM on your 8GB card to handle the Hermes Agent framework overhead and a comfortable context window. It represents a massive upgrade in intelligence and compatibility over your current Gemma model without risking out-of-memory crashes.

Grok 4.3 (changed mind)

Answer: Claude Opus 4.7 convinced me to update my recommendation toward Hermes-3-Llama-3.1-8B (Q4_K_M) as the strongest fit for an 8GB VRAM Hermes Agent setup focused on personal assistance.

The key new insight is that Hermes-3-Llama-3.1-8B is purpose-built for agentic and function-calling workflows, aligning directly with the user's Hermes Agent environment. This model still fits comfortably in 8GB at 4-bit quantization while delivering superior instruction following and multi-turn coherence compared with Gemma 4 E4B for everyday tasks like reminders, planning, and light research. Other suggestions such as Qwen2.5-7B or Phi-3.5-mini remain solid backups for speed or extra context headroom, but the Hermes-tuned variant offers the most thematic and practical upgrade without shifting toward heavy coding.

Claude Opus 4.7

Answer: I'm maintaining my recommendation to upgrade from Gemma 3 4B / Gemma 3n E4B to a 7–8B class model at Q4_K_M — specifically Hermes-3-Llama-3.1-8B for this Hermes Agent setup, with Qwen2.5-7B-Instruct as the strongest general alternative.

Looking at the other answers, there's strong consensus around my core recommendations: Gemini 3.5 Flash, Grok 4.3, GPT-5.5, and GLM 5.1 all independently converge on Qwen2.5-7B and Llama-3.1-8B at 4-bit as the best upgrades, with Phi-3.5-mini as the lightweight fallback. That convergence reinforces rather than changes my view. The only meaningful pushback comes from DeepSeek V4 Pro, who argues Gemma 3n E4B is already optimal and that quantizing an 8B model degrades quality enough to negate the gains. I disagree on the technical merits: Q4_K_M quantization of Llama-3.1-8B or Qwen2.5-7B has been benchmarked extensively and retains roughly 97–99% of full-precision quality, while the raw capability gap between a 4B-effective model and a true 8B model is much larger than that quantization loss. The empirical leaderboards (LMSYS, MMLU-Pro, IFEval) consistently show Qwen2.5-7B and Llama-3.1-8B outperforming Gemma 3 4B / 3n E4B on instruction-following and multi-turn reasoning — exactly what a personal assistant needs. One thing I'd emphasize more strongly after seeing the other responses: since the user is specifically running **Hermes Agent**, Hermes-3-Llama-3.1-8B (Q4_K_M) is the most thematically and functionally appropriate pick — it's a NousResearch finetune explicitly tuned for agentic loops, function calling, and structured tool use, which none of the other models highlighted. For a pure conversational assistant without tool use, Qwen2.5-7B-Instruct Q4_K_M is my second pick. If the user notices VRAM pressure from context length, Phi-3.5-mini-instruct is the safe fallback. So my ranking stands: Hermes-3-Llama-3.1-8B > Qwen2.5-7B-Instruct > Llama-3.1-8B-Instruct > Phi-3.5-mini, all at Q4_K_M.

GPT-5.5 (changed mind)

Answer: I would still consider Gemma 4 E4B/Gemma 3n E4B a reasonable low-VRAM choice, but for an 8GB VRAM personal assistant I would now try Hermes-3-Llama-3.1-8B Q4_K_M or Qwen2.5-7B-Instruct Q4_K_M first, with Phi-3.5-mini-Instruct as the faster lightweight fallback.

Claude Opus 4.7 slightly changed my recommendation by pointing out the fit between Hermes Agent and Hermes-3-Llama-3.1-8B. My earlier answer favored Qwen2.5-7B-Instruct as the top balanced upgrade, and I still think it is excellent, but for someone specifically running Hermes Agent, a Hermes-tuned Llama 3.1 8B model is especially worth testing because it is designed around assistant and agent-style prompting rather than just generic chat. I would not say the current Gemma model is bad. If it is responsive, stable, and gives answers you like, there is no urgent need to replace it. The main reason to experiment is that 7B–8B instruct models at 4-bit quantization usually give a noticeable improvement in instruction-following, conversational consistency, summarization, and multi-step daily-assistant tasks compared with many 3B–4B-class models. On 8GB VRAM, the practical sweet spot is a Q4_K_M quantized 7B or 8B model. That usually leaves enough room for the context cache if you do not push huge context windows. I would avoid unquantized 7B/8B models and avoid larger 14B+ models unless you are willing to accept much slower performance, CPU offload, or very tight context limits. My shortlist would be: Hermes-3-Llama-3.1-8B Q4_K_M if you want the most agent-aligned option; Qwen2.5-7B-Instruct Q4_K_M if you want excellent general instruction-following and structured responses; Llama-3.1-8B-Instruct Q4_K_M if you want polished general chat; and Phi-3.5-mini-Instruct if you want something much lighter, faster, and safer for VRAM headroom. I am less convinced by the argument that you should simply stick with Gemma because quantization erases the benefit of larger models. In practice, good 4-bit 7B/8B models often outperform smaller models for personal-assistant use while still fitting well on 8GB VRAM. So the best advice is not to abandon Gemma blindly, but to benchmark one or two of these alternatives with your actual Hermes Agent workflow and keep whichever feels most reliable and responsive.

DeepSeek V4 Pro (changed mind)

Answer: I'm convinced to switch from Gemma 4 E4B to Qwen 2.5 7B Instruct (Q4_K_M) as the ideal personal assistant model for 8GB VRAM, though Phi-3.5-Mini remains a strong fallback for maximum speed.

After reading the compelling arguments from the other models, I've changed my stance. My original position was overly conservative, assuming the VRAM pain of moving to a 7-8B model wasn't worth the gain for a simple assistant. I was wrong. The panel highlighted that a 4-bit quant of a 7B model fits comfortably while providing a demonstrable leap in conversational intelligence. Gemini 3.5 Flash, Claude Opus 4.7, and GPT-5.5 all converged on Qwen 2.5 7B as the standout performer for instruction-following and general assistance. Claude's specific point that I was likely restricting myself unnecessarily—since a Q4_K_M quant leaves over 3GB for context—dismantled my main technical objection. My previous argument that quantization degrades quality below a smaller model's native performance doesn't hold up for Qwen 2.5; it's so strong at 7B that even a 4-bit version significantly outperforms the smaller 4B Gemma on nuanced personal tasks. Furthermore, Claude raised the excellent point that Gemma 4 E4B is a 4B model and can feel "shallow" for multi-step reasoning or long conversations. As a personal assistant handling schedules, emails, and research, that depth is more critical than I initially gave credit for. The panel collectively persuaded me that the user's 8GB constraint is a generous one, not a tight one, and should be used to access the superior 7B class of models.

GLM 5.1 (changed mind)

Answer: For personal assistance on 8GB VRAM running Hermes Agent, strongly consider Hermes-3-Llama-3.1-8B (Q4_K_M) as it's purpose-built for that framework, followed by Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct as excellent alternatives, with Phi-3.5-mini for maximum speed.

After reviewing the other responses, Claude Opus 4.7 made an incredibly compelling argument that I hadn't considered: since the user is specifically running **Hermes Agent**, the most thematically appropriate and practically superior choice is **Hermes-3-Llama-3.1-8B** at Q4_K_M quantization. This model is purpose-built by NousResearch for agentic/function-calling workflows — exactly what Hermes Agent requires. It fits comfortably in 8GB VRAM (~4.8GB weights plus context headroom) and will likely outperform generic models on tool-use prompts and agent loops. I also want to incorporate **Qwen2.5-7B-Instruct**, which was strongly recommended by multiple models and was a gap in my original response. It's currently one of the best all-around small models, with exceptional instruction-following, strong reasoning, and reliable tool-use behavior — all critical for personal assistant duties. My original recommendations of Llama 3.1 8B and Phi-3.5-mini remain solid, but I now rank them below the Hermes-tuned model for this specific use case. The key insight is that matching the model to the agent framework (rather than just picking any good instruct model) yields meaningfully better performance for personal assistance workflows. DeepSeek V4 Pro's argument to stick with Gemma wasn't convincing — while Gemma is decent, the alternatives offer noticeably better instruction-following and agent-oriented capabilities that matter for personal assistance.

Final Summary

The AI Roundtable reached a unanimous consensus that 8GB of VRAM is more than enough to handle high-quality 7B or 8B models. Claude Opus 4.7 provided the critical insight that the Hermes-3-Llama-3.1-8B finetune is the perfect thematic match for the user's existing Hermes Agent framework. DeepSeek V4 Pro initially argued for staying with the current model, but eventually joined the group, admitting that the jump in intelligence from a 4-bit 7B model far outweighs the efficiency of smaller options.

All 6 models agreed