If you are building a PC for running large language models locally, the GPU VRAM question will come up before anything else. Should you stop at 16GB or spend more for 24GB? The answer depends on what you plan to run, at what context length, and how much you care about hitting a ceiling six months from now.
What VRAM Actually Does in a Local LLM Setup?
Before getting into numbers, it helps to understand why VRAM matters so much. According to Vipera Tech, VRAM is the GPU’s fast working memory, it is where your AI model weights, activations, and batches live while the GPU is working. If everything fits in VRAM, performance is smooth. If it does not, the system offloads parts to system RAM or SSD, which causes significant slowdowns and stutters.
In local LLM tools like llama.cpp and Ollama, VRAM usage has three parts: a backend baseline overhead, the model weights themselves, and the KV (key-value) cache, which grows linearly with your context window size. According to LocalLLM.in’s benchmarks with llama.cpp, a 27B parameter model at Q4_K_M quantization needs 16.10 GB for weights alone. Add a 32K context window and the KV cache pushes total usage to 18.06 GB. Push context to 64K and it climbs to 20.06 GB.
That gap, 18 GB at 32K context versus 20 GB at 64K context is exactly where the 16GB versus 24GB decision starts to matter.
What Q4_K_M Quantization Changes?
If we go by the discussion around local LLM VRAM requirements assumes Q4_K_M quantization, and for good reason. According to LocalLLM.in, Q4_K_M compresses model weights to 4-bit precision, reducing VRAM requirements by approximately 75% compared to full FP16 precision, with minimal output degradation. It is considered the recommended quantization for most users because it hits the best balance between memory efficiency and model quality.
Without Q4_K_M, a 27B model that uses 16.10 GB in VRAM would require roughly 54 GB in full FP16, well beyond any single consumer GPU. Quantization is what makes local LLMs possible on consumer hardware in the first place, so all VRAM numbers discussed below assume Q4_K_M unless noted otherwise.
VRAM Numbers by Model Scale
LocalLLM.in benchmarked several popular models directly under llama.cpp at both 32K and 64K context windows. Here is what the data shows:
9B Parameter Models
Testing with Qwen3.5-9B at Q4_K_M:
- File size: 5.29 GB
- Weights VRAM: 5.80 GB
- KV cache at 32K context: 0.98 GB = Total: 6.78 GB
- KV cache at 64K context: 1.97 GB = Total: 7.77 GB
At 32K context, this fits easily on an 8 GB card like an RTX 4060. At 64K context, the 7.77 GB total gets dangerously close to the 8 GB physical limit, especially if the display is running on the same GPU. A 16 GB card has no problem here at all.
27B Parameter Models
This is where 16 GB starts to feel tight. Testing with Qwen3.5-27B at Q4_K_M:
- Weights VRAM: 16.10 GB
- KV cache at 32K context: 1.96 GB = Total: 18.06 GB
- KV cache at 64K context: 3.96 GB = Total: 20.06 GB
Testing with GLM-4.7-Flash (also ~27B) at Q4_K_M:
- Weights VRAM: 17.72 GB
- KV cache at 32K context: 1.63 GB = Total: 19.35 GB
- KV cache at 64K context: 3.28 GB = Total: 21.00 GB
According to LocalLLM.in, a 24 GB GPU (like an RTX 3090 or RTX 4090) runs these models completely in VRAM even at a 64K context with no latency issues. A 20 GB card (like the RX 7900 XT) can handle them at 32K context but sits right at the edge at 64K. A 16 GB card cannot run 27B models fully in VRAM at any usable context window, the model weights alone at 16.10–17.72 GB already exceed or nearly fill the 16 GB limit.
35B Parameter Models (MoE Architecture)
Testing with Qwen3.5-35B-A3B at Q4_K_M:
- Weights VRAM: 21.06 GB
- KV cache at 32K context: 0.61 GB = Total: 21.67 GB
- KV cache at 64K context: 1.24 GB = Total: 22.29 GB
Note the unusually small KV cache footprint compared to the 27B models above. This is because Qwen3.5-35B-A3B is a Mixture-of-Experts (MoE) model using Grouped Query Attention (GQA), which drastically reduces the KV cache footprint relative to parameter size. The model still requires about 21 GB just for weights, so it does not fit on 16 GB at all. On a 24 GB card, it fits, but just barely. Running a display concurrently on the same GPU at 64K context could push usage over the 24 GB limit and force partial CPU offloading.
There is also a notable performance advantage to the MoE architecture. Despite occupying significantly more VRAM than a 9B model (approximately 21.1 GB versus 5.8 GB for weights), the 35B-A3B reportedly delivers higher throughput (tokens per second) and better time-to-first-token on the same GPU, because only a small fraction of its parameters (approximately 3B active per token) participate in each forward pass. This means on a 24 GB GPU, the 35B-A3B is not just a capability step up from a 9B model, it may also be faster in generation.
Hidden VRAM Multiplier
One of the most overlooked factors in VRAM planning is the context window. The model file size tells you the minimum VRAM you need, but the context window determines how much extra VRAM the KV cache will consume on top of that.
For a 27B model, going from 32K to 64K context adds almost exactly 4 GB of additional VRAM (3.96 GB specifically for Qwen3.5-27B). The Qwen3.5-27B GGUF file is 15.6 GB, but the model requires over 20 GB of VRAM at 64K context. File size alone does not predict real-world VRAM needs.
This is critical for anyone buying a 16 GB card and assuming they can run “up to 16 GB of models.” The context window can push that number well beyond what the card physically holds.
How 16 GB Performs in Practice
If we go by the results from LocalLLM who tested multiple models on an NVIDIA T4 (16 GB VRAM) using Ollama with a llama.cpp backend. The T4’s performance is described as comparable to consumer RTX 2060/2070 GPUs. Key results from practical workloads:
GPT-OSS 20B at 60K context used 13.7 GB VRAM and delivered 42.18 tokens per second on a coding task. On a summarization task processing 48,822 actual tokens, generation speed dropped to 28.87 tokens per second a 32% slowdown due to the attention mechanism computing over a fully filled KV cache.
Qwen3 14B at 4K context used 9.2 GB VRAM and generated at 14.86 tokens per second. At 32K context, VRAM jumped to 13.6 GB and generation speed fell to 9.59 tokens per second.
Apriel 1.5 15B-Thinker at 4K context used 9.9 GB VRAM at 14.84 tokens per second. At 20K context, VRAM reached 14.3 GB and speed dropped to 6.94 tokens per second.
Pushing GPT-OSS 20B to 120K context caused total memory to reach 14.1 GB VRAM plus 2.6 GB system RAM, and generation speed collapsed to 7.05 tokens per second a 6x slowdown compared to 60K context.
The practical ceiling for a 16 GB card with current top-tier models is roughly a 20B MoE model at 60K context, or a 14B–15B dense model at 16K–32K context. Beyond those configurations, performance degrades sharply or the model simply does not fit.
What 24 GB Opens Up?
A 24 GB card removes the constraints that define the 16 GB experience. According to reports, a single 24 GB GPU (RTX 3090 or RTX 4090) can:
- Run 27B parameter models at 64K context with no performance compromise
- Run 35B MoE models at 32K context (21.67 GB total) without spilling into system RAM
- Handle 4 simultaneous users on a 35B MoE model (Qwen3.5-35B-A3B) with a combined KV cache of just 0.80 GB for 4 users at 8K context each, for a total of ~22.05 GB still under the 24 GB ceiling
On the concurrency test, LocalLLM.in reported that 4 simultaneous users on a 24 GB GPU with Qwen3.5-35B-A3B at 8K context each saw an average of approximately 18.1 tokens per second per user and a time-to-first-token of 6.2 seconds, while total combined system output reached approximately 30.3 tokens per second. The GPU stayed at roughly 22.05 GB VRAM below the limit while handling all four requests.
The 35B MoE model at 24 GB also opens the door to extended context with the RTX 5090. On 32 GB VRAM, LocalLLM.in pushed the same Qwen3.5-35B-A3B model to a 262K context window. Total VRAM at 262K context reached approximately 27.3 GB. Generation (decode) speed was 203.1 tokens per second barely different from the 204.7 tokens per second at 32K context. The time-to-first-token increased from 29.0 ms to 42.3 ms, a 13.3 ms difference. The main cost was prefill speed, which dropped from 792.9 tokens per second to 543.3 tokens per second.
The Model Benchmark Picture for 16 GB Cards
For users with a 16 GB card, the model options in 2026 are not as limited as the VRAM math might suggest. According to LocalLLM.in’s testing and benchmark analysis, three models stand out in this tier:
GPT-OSS 20B scores 52.1% on the Artificial Analysis Intelligence Index, 40.7% on the Coding Index, and 77.7% on LiveCodeBench. It achieved perfect scores on spatial logic tests in custom cognitive challenges.
Apriel-v1.5-15B-Thinker scores 51.6% on the Intelligence Index, 72.8% on LiveCodeBench, and 71.3% on GPQA Diamond. It also carries native vision support, which no other model in the tested 16 GB tier offers.
Qwen3 14B (Reasoning mode) scores 96.1% on Math 500 and 76.3% on AIME 2025, making it the strongest option specifically for advanced math and knowledge retrieval tasks in the 16 GB class.
For reference, GPT-OSS 20B at 60K context uses 13.7 GB VRAM, which leaves some headroom on a 16 GB card. Apriel 1.5 at 4K context uses just 9.9 GB. Qwen3 14B at 4K context uses 9.2 GB.
| Use Case | 16 GB (e.g., RTX 4080) | 24 GB (e.g., RTX 3090 / 4090) |
|---|---|---|
| 9B models, 32K context | Fits easily (~6.78 GB) | Fits with lots of headroom |
| 14B–15B models, 16K context | Fits (~13–14 GB) | Fits with headroom |
| 20B MoE models, 60K context | Fits (~13.7 GB) | Fits with headroom |
| 27B models, 32K context | Does not fit (18+ GB needed) | Fits (~18 GB) |
| 27B models, 64K context | Does not fit (20+ GB needed) | Fits (~20 GB) |
| 35B MoE models, 32K context | Does not fit (21.67 GB needed) | Fits (~21.67 GB) |
| 35B MoE models, 64K context | Does not fit (22.29 GB needed) | Fits (~22.29 GB) |
| LoRA/QLoRA fine-tuning | Possible but constrained | Safer baseline |
| Full fine-tuning | Not recommended | Minimum recommended |
Note: All the VRAM numbers sourced from LocalLLM benchmarks at Q4_K_M quantization.
For pure inference on current-generation models up to about 20B parameters (especially MoE architectures), 16 GB is sufficient. The GPT-OSS 20B model at 60K context on a 16 GB card delivers 42 tokens per second, that is responsive and usable. The models available in this tier in 2026, particularly GPT-OSS 20B and Apriel 1.5, score in the 51–52% range on the Artificial Analysis Intelligence Index, which is competitive.
The limitation shows up when you want to run 27B or larger dense models fully in VRAM, or when you want to push context windows beyond 32K on heavier models. At that point, 16 GB forces layer offloading to system RAM, which degrades generation speed meaningfully.
24 GB removes that ceiling for the current generation of consumer-tier models. A 35B MoE model at 32K context fits with about 2.3 GB of VRAM to spare. Multi-user serving at 4 concurrent users on a 35B model fits within 22 GB. And for anyone thinking about fine-tuning or running 27B dense models at extended context, 24 GB is the practical floor.
FAQs
The primary advantage of 24GB VRAM is the ability to run larger, more capable models such as 32B to 35B parameter variants entirely on your GPU. While 16GB is excellent for medium-sized models, 24GB serves as the “ceiling” for most single-GPU consumer hardware, offering greater flexibility and fewer memory compromises.
Yes, 16GB is widely considered a “sweet spot” for many users. It comfortably handles 14B–25B parameter configurations, allows for larger context windows, and provides enough overhead to keep other tools or browser tabs open while running your model.
VRAM capacity primarily determines if a model can run fully on the GPU, which is critical for performance. By keeping the model entirely in VRAM, you avoid the latency caused by offloading data to slower system RAM, ensuring real-time token generation.
