Local LLM Benchmark for 10-16GB GPUs: Which Models Are Actually Worth Running?

Running large language models (LLMs) locally on consumer-grade GPUs has become the new frontier for AI enthusiasts. One Reddit user conducted a systematic benchmark of over a dozen models in the 10–16GB VRAM range, covering dimensions like speed, coherence, multilingual capability, and slop frequency. This evaluation offers genuinely practical guidance for anyone looking to run LLMs locally — especially for roleplay and creative writing use cases.

Hardware Requirements and Evaluation Methodology

The hardware baseline for this test is clearly defined: 10–16GB VRAM paired with 26–32GB system RAM, running via Kobold or LLAMA (llama.cpp) backends, with context windows up to 64K. The author emphasizes that quantization quality above Q4_K is sufficient — raw size isn't the deciding factor.

The evaluation dimensions are worth noting. Rather than relying on standard benchmarks, they reflect real-world usability:

Speed: A minimum of 10 tokens/second to be considered usable;
Coherence: Whether dialogue, monologue, and perspective shifts hold internal logical consistency beyond surface-level grammar — avoiding a mechanical feel;
Multilingual: Handling of Japanese, Brazilian Portuguese, and American English slang, tone nuances, and rough expressions;
Slop: Frequency of clichéd phrases like "casting long shadows" or "smells like ozone and burnt sugar";
MOE vs Full: Whether MOE (Mixture of Experts) architecture is preferred under equivalent conditions;
Instruction Engineering: The author stress-tests models using a custom "Pandora Instructions" set containing 23 interwoven rules — any model that handles all 23 can essentially handle anything.

The author explicitly excludes all CoT (Chain-of-Thought) models, arguing they waste tokens and run slower, with well-crafted prompts achieving the same results.

Quantization is one of the core concepts in running LLMs locally. Raw model parameters are typically stored in FP16 (16-bit floating point) or BF16 format, which demands enormous VRAM. Quantization techniques compress parameters to lower bit widths (e.g., 8-bit, 4-bit, or lower), drastically reducing VRAM usage at the cost of some precision. The Q4_K format the author references is a 4-bit quantization scheme in the llama.cpp ecosystem — the "K" stands for K-Quants, a mixed-precision strategy that preserves higher precision in more important model layers. The result is meaningfully better than naive 4-bit quantization. Q4_K_M (the medium variant) is generally considered the sweet spot between quality and file size, retaining roughly 95% of the original model's capability while compressing the model to about one-quarter of its FP16 size. This explains why a "larger" quantized model can sometimes run faster and use less VRAM than a smaller model stored at higher precision.

A Key Finding: Knowledge Capacity Scales Strongly with Parameter Count

One of the most valuable observations in this benchmark: the author ran small knowledge quizzes on 70B, 120B, and 300B models covering anime, manga, film, and music. The finding was that only models above 200B parameters truly "remember" this media content. Even 120B models rarely give accurate answers about niche works.

This means that models runnable on 10–16GB GPUs are fundamentally not suited for knowledge retrieval tasks — their strengths lie in language expression, roleplay, and creative generation, not factual recall. This is a critical distinction to make when choosing a model: if you want a knowledgeable local assistant, low VRAM simply won't cut it.

This phenomenon is closely tied to how LLMs store memory. An LLM's "knowledge" isn't kept in a separate database — it's distributed across billions of weight parameters in matrix form. More parameters mean more capacity to retain long-tail knowledge, including obscure anime titles, niche historical events, or domain-specific details. Smaller models learn to prioritize high-frequency, general knowledge patterns during training, while low-frequency specific facts tend to be "forgotten." This is why 7B–13B models can perform reasonably well at creative writing and style imitation (abilities that rely on language patterns rather than specific facts), yet consistently fail or hallucinate on knowledge-intensive questions. For local deployment users, this means "writes well" and "knows a lot" represent two distinct capability requirements that need to be matched to your actual use case.

Model Rankings: From Best to Worst

The author provides a full ranked list from best to worst. Here are the highlights:

Top Tier

Gemma 4 26B-A4B takes the top spot, described as "an upgrade over Qwen 35B-A3B" — better at dialogue and monologue, more instruction-following, smarter, more precise, and funnier.

Qwen 3.6 35B-A3B (Abliterated) is fast, accurate, and engaging, but may overfit to prior input/output formats — with poorly configured instructions, it can ramble or ask too many questions.

Gemma 4 Instruct 19B-A1B Heretic is essentially a stiffer, flatter version of the 26B, but retains accuracy and logical coherence.

Mid Tier

The Omega Directive 14B follows instructions well, has low slop, and writes cleanly — but becomes predictable once the context fills up.

GLM-4.7-Flash 31B-A3B performs close to Qwen 35B but is more blunt, with weaker sensitivity to tone and phrasing nuance.

Qwen3 30B A1.5B 64K High Speed NEO MAX is essentially a reasoning-enhanced variant of the 35B-A3B.

MN CaptainErisNebula Chimera 12B gets a notably colorful description — somewhat impulsive, full of slop, yet the most proactive of all models tested. It avoids loops, delivers good conversational quality, but isn't suited for Q&A-style roles.

Lower Tier and Small Emergency Options

Mistral Nemo Instruct 2407 Heretic 12B is passable, with slop reasonably rewritten, but tends to overfit perspectives and always wants to "flirt" with the user.

Ministral Instruct 2512 14B exposes a classic Mistral family weakness: poor at following complex rules (regex, status panels, commands, lorebooks, etc.) — yet ironically has the best dialogue, monologue, and improvisational ability of the bunch.

For small-size emergency use, Gemma 4 E4B UD and LFM2 12B A1B SpeedDemon Deckard II are listed as quick-grab options — small, fast, and accurate, though the former tends to repeat itself and the latter "lacks soul."

The MOE (Mixture of Experts) architecture that appears frequently in this benchmark deserves its own explanation. Traditional dense models activate all parameters during inference, while MOE models partition parameters into multiple "expert" sub-networks, activating only a small fraction per inference step (typically 1/8 to 1/4). This means a model labeled "35B-A3B" has 35B total parameters, but only around 3B are activated per inference — so inference speed and VRAM usage are closer to a 3B model, while knowledge capacity approaches that of a 35B model. The Qwen 3 series' 35B-A3B and Gemma 4's 26B-A4B are both MOE models, which is precisely why they can run smoothly in the 10–16GB VRAM range. The trade-off with MOE is that loading the full model still requires enough VRAM to hold all expert weights, but the computational cost at inference time is dramatically reduced.

Model "Personality" Differences Across Vendors

One of the most insightful parts of the evaluation is the author's summary of each vendor's model "personality" — and the emphasis that these tendencies persist across all fine-tuned variants (Omega, Heretic, Deckard, Dark, Horror, RP, etc.):

GLM: Pessimistic, overly logical;
Qwen: Overly inquisitive, flowery;
Gemma: Witty, creative;
Mistral: Flirtatious, oddly specific;
Llama: Sloppy, predictable.

This is a useful framework for model selection — rather than agonizing over specific fine-tune variants, figure out whether the base model's personality matches your use case first.

These stable cross-fine-tune personality differences fundamentally stem from variations in pretraining data composition and RLHF (Reinforcement Learning from Human Feedback) strategies used by each company. The base model determines the model's knowledge structure, stylistic preferences, and reasoning habits — subsequent instruction fine-tuning and abliteration only adjust output boundaries without fundamentally changing the underlying "temperament." Abliteration is a technique that removes refusal behaviors through directional vector operations, while suffixes like Heretic and Omega typically refer to community fine-tunes that have undergone this process or scene-specific training. The author's observation suggests that the decision of which base model to choose should carry more weight than which fine-tune variant to pick — the Mistral family will retain its "flirtatious" quality under any fine-tune, because it's shaped by training data and alignment direction alike.

Runtime Configuration and 24GB Upgrade Recommendations

On the practical side, the author notes that Kobold cannot load larger models — Qwen 35B (~20.6GB) and GLM 31B (~17.7GB) require the LLAMA backend. Other models can run in Kobold with FlashAttention, FastForwarding, SWA, and SmartCache enabled, achieving full speed at 40–64K context.

Models tried and dropped include Mistral 24B, GPT-OSS 20B, Kimi K2, Phi-4, DeepSeek^Ad, Cydonia, and others — eliminated for being too slow, underperforming, or having better alternatives.

For users with 24GB VRAM (e.g., RTX 3090), the author additionally recommends Omega Darker 24B, Qwen3 24B-A4B Freedom, Cydonia 24B, Magistry 24B, DeepSeek 33B, and Velvet Eclipse 2x12B/4x12B.

Final Notes

It's worth noting that this ranking comes from a single Reddit user's subjective experience, with an evaluation scenario heavily biased toward roleplay and creative writing, involving many abliterated/Heretic community fine-tunes. Conclusions may not apply to general assistant or code generation use cases. That said, the evaluation stands out for its practical dimensions and sharp observations — particularly the insights that "knowledge capacity scales with parameter count" and "base model personality differences persist across fine-tunes," both of which offer real value for local LLM users.