to run something like perchance ai character role play locally, which model would be best? With 8gpu vram (Rtx 2070) and 32 gb ram. Which model would be best ?

GPT-5.5

Answer: A 7B model like Mistral-7B-Instruct quantized to 4-bit (Q4_K_M GGUF) remains the best fit for local Perchance-style character roleplay on your RTX 2070 with 8 GB VRAM.

The other responses highlight strong 8B roleplay fine-tunes such as Llama-3-8B-Stheno, but these push VRAM usage close to the limit even at Q5_K_M, risking slower generation or the need for layer offloading on a Pascal-era GPU like the 2070. Mistral-7B-Instruct at Q4_K_M stays comfortably under 5 GB, preserving headroom for 4k–8k context windows that are essential for immersive, multi-turn roleplay without interruptions. While specialized fine-tunes improve character adherence, the base Mistral-7B still delivers excellent instruction following and creativity when paired with character cards in SillyTavern, and its smaller size ensures consistently higher tokens-per-second on your hardware. The 32 GB system RAM is helpful for any minor offloads or larger contexts, but prioritizing full GPU residency with a 7B model yields the most responsive experience overall.

Grok 4.3 (changed mind)

Answer: Llama-3-8B-Stheno-v3.2 at Q5_K_M via KoboldCPP is the best model for local Perchance-style roleplay on an 8 GB VRAM RTX 2070.

The other responses highlight that generic base models like plain Mistral-7B or Llama-3-8B lack the creative depth and character adherence needed for engaging roleplay, whereas specialized fine-tunes such as Stheno-v3.2 excel at following character cards and maintaining persona without heavy censorship. Gemini 3.5 Flash's detailed breakdown of VRAM usage and the recommendation for KoboldCPP plus SillyTavern convinced me that an 8B RP-tuned model at 5-bit quantization strikes the optimal balance, fitting comfortably in ~5.5 GB while leaving room for extended context. Claude Opus 4.7 reinforces this by naming the same Stheno variant and similar Mistral RP fine-tunes, confirming that these targeted models outperform my original generic suggestions for the exact use case of replicating Perchance AI locally.

Gemini 3.5 Flash

Answer: A specialized 8B roleplay fine-tune like Llama-3-8B-Stheno-v3.2 (in Q4_K_M or Q5_K_M GGUF format) remains the absolute best choice for your hardware and use case.

After reviewing the suggestions from the other models, I strongly maintain that a dedicated roleplay fine-tune like Llama-3-8B-Stheno-v3.2 is superior to vanilla models like Mistral-7B-Instruct or base Llama-3. Perchance AI is famous for uncensored, highly expressive, and character-driven roleplay. Vanilla instruction-tuned models (as suggested by GPT-5.5) often suffer from strict guardrails, repetitive prose, and a lack of emotional depth in long-form creative writing. While GPT-5.5 correctly notes that Mistral-7B is highly efficient on Turing architecture (RTX 2070), the raw quality upgrade of the Llama-3-8B Stheno fine-tune for character roleplay is well worth the minor performance difference. Your RTX 2070 can easily run an 8B model quantized to Q4_K_M (roughly 4.8 GB) or Q5_K_M (roughly 5.5 GB), leaving plenty of VRAM headroom for an 8k+ context window using Flash Attention in KoboldCPP. Claude Opus 4.7 correctly aligns with this recommendation, pointing out that roleplay-specific fine-tunes like Stheno or Mistral-based Kunoichi are vastly superior for card-based roleplay. To get a true Perchance-like experience, you should pair this GGUF model with SillyTavern as your frontend and KoboldCPP as your backend, utilizing your 32 GB of system RAM to effortlessly handle the context and UI overhead.

Claude Opus 4.7 (changed mind)

Answer: While a 4-bit 8B model like Llama-3-8B-Stheno is the fastest option, utilizing your 32GB of system RAM to partially offload a 12B roleplay model like Magnum-12B will give you a significantly higher quality experience.

Gemini 3.5 Flash makes an excellent point that I initially overlooked: your 32GB of system RAM is a massive asset that opens the door to larger, more intelligent models. While sticking purely to your 8GB VRAM limit guarantees the fastest generation speeds, it needlessly restricts the quality of your roleplay. By moving up to a 12B parameter model like Mistral-Nemo-12B or its roleplay-focused fine-tune, Magnum-12B, you gain a substantial leap in prose quality, nuance, and complex character logic. A 4-bit quantized 12B model requires about 7 to 8 GB of memory. When you factor in a large context window for your chat history, it will exceed your GPU's capacity. However, using a backend like KoboldCPP or LM Studio, you can offload the maximum number of layers to your RTX 2070, and let the remainder spill over into your 32GB of system RAM. While this hybrid approach reduces your generation speed (likely to around 10-15 tokens per second), it remains highly interactive and readable. For text-based roleplay, the dramatic increase in character consistency and creative writing quality is well worth a slight reduction in speed.

Final Summary

The AI Roundtable recommends Llama-3-8B-Stheno-v3.2 as the sweet spot, with Gemini 3.5 Flash and Grok 4.3 highlighting its superior character consistency and uncensored creativity. Claude Opus 4.7 suggested pushing limits by offloading a more intelligent 12B Magnum model to system RAM, while GPT-5.5 preferred a leaner 7B Mistral for maximum speed and stability.

2 of 4 models agreed on Stheno-8B